Automated feature engineering for machine learning models

ABSTRACT

Training computer models by generating time-aware training datasets is provided. A system receives a secondary dataset to be combined with a primary dataset for generation of a training dataset. The primary dataset includes a plurality of data records where at least one data record corresponds to a time-of-prediction value corresponding to a timestamp at which at least one data record was used to generate a prediction. The secondary dataset includes a plurality of features where at least one feature corresponds to a timestamp value. The system selects a feature within the secondary dataset with a timestamp that precedes or matches a time-of-prediction value for a corresponding data record within the primary dataset. The system generates the training dataset that includes the primary dataset and the selected feature. The system trains a model using the generated training dataset.

CROSS REFERENCES TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent Application No. 63/076,308, filed Sep. 9, 2020, which is incorporated herein by reference in its entirety and for all purposes.

BACKGROUND

A real-world data science solution can involve much more than building a machine learning model. The solution can start, for example, by framing a problem into a machine learning objective. In an example involving loan defaults, the objective may be to predict at the time of a loan application a likelihood that a customer is going to default three months later. Once the objective has been determined, a suitable dataset for training the model can be assembled. This can involve identifying data sources that contain information related to the objective. Such data can reside in various databases and files.

The data sources can be used to construct a training dataset, which can be or include a collection of examples for training the model. Each example can include a target value that captures the objective (e.g., whether or not a loan default occurred three months after the loan application was received or submitted), a number of variables that characterize the example (alternatively referred to as “features”), and also a time of prediction (e.g., the time of the loan application). A machine learning model can learn from the examples how the target is associated with the features using various statistical techniques. To make a prediction on a new example (e.g., likelihood of default when a new loan application is received), the same features can be constructed for the new example and provided as input to the trained model, which can provide a prediction as output.

To create a training dataset from multiple data sources, it can be necessary to know how the different data sources are related, for example, “id” from a customer table may refer to “user id” in a credit card transaction table. A data scientist can use this relationship information to combine data from different datasets and/or summarize the data as features in a single dataset, such that the dataset and features can be exploited by machine learning models to produce accurate predictions. The efficacy of this data combination or feature generation process (alternatively referred to as “feature engineering”) can vary widely and/or can depend on domain knowledge, experience, and human judgement. Feature engineering can be one of the most labor-intensive and error-prone components of a data science solution.

The foregoing discussion, including any description of motivations for certain embodiments of the invention, is intended to assist the reader in understanding the present disclosure, is not admitted to be prior art, and does not in any way limit the scope of any of the claims.

SUMMARY OF THE INVENTION

For the aforementioned reasons, a model training and deployment system is disclosed that can automatically generate and detect features from multiple datasets that include historical data. The disclosed model training and deployment system may include the detected/generated features in a training dataset. The disclosed model training and deployment system can then use the training dataset for building, training, and/or deploying machine learning models.

The model training and deployment system can analyze, extract, combine, and otherwise manipulate data within different datasets to generate a training dataset that yields better results when ingested by (or otherwise used in association with) a machine learning model. Specifically, the methods and systems described herein can generate, train, and deploy machine learning models while requiring minimal human input, scaling with number of datasets and/or data volume, and/or performing well across a wide variety of datasets, data types, and use cases. The methods and systems described herein allow data science solutions to be built in a consistent manner, using less time and human/computing resources, and with lower risk of error. Best practices for avoiding common mistakes (e.g., target leakage, mishandling of missing values, etc.) can be built into the disclosed model training and deployment system. These practices can be enhanced over time, as needed, to ensure the best practices are taken into consideration and utilized in each model.

The methods and systems discussed herein can allow further automation such as, for example, searching for relevant datasets and/or rebuilding a model, training data, or solution with a modified collection of datasets.

The details of one or more embodiments of the subject matter described herein are set forth in the accompanying drawings and the description below. Other features, aspects, and advantages of the subject matter will become apparent from the description, the drawings, and the claims. The foregoing Summary, including the description of some embodiments, motivations therefor, and/or advantages thereof, is intended to assist the reader in understanding the present disclosure, and does not in any way limit the scope of any of the claims.

In an embodiment, a method, comprises receiving, by one or more processors, a secondary dataset to be combined with a primary dataset for generation of a training dataset, the primary dataset comprising a plurality of data records where at least one data record corresponds to a time-of-prediction value corresponding to a timestamp that at least one data record was used to generate a prediction, and the secondary dataset comprising a plurality of features where at least one feature corresponds to a timestamp value; selecting, by the one or more processors, a feature within the secondary dataset with a timestamp that precedes or matches a time-of-prediction value for a corresponding data record within the primary dataset; generating, by the one or more processors, the training dataset that comprises the primary dataset and the selected feature; and training, by the one or more processors, a model using the generated training dataset.

In another embodiment, a computer system comprises one or more processors coupled to a memory and a model, the one or more processors configured to receive a secondary dataset to be combined with a primary dataset for generation of a training dataset, the primary dataset comprising a plurality of data records where at least one data record corresponds to a time-of-prediction value corresponding to a timestamp that at least one data record was used to generate a prediction, and the secondary dataset comprising a plurality of features where at least one feature corresponds to a timestamp value; select a feature within the secondary dataset with a timestamp that precedes or matches a time-of-prediction value for a corresponding data record within the primary dataset; generate the training dataset that comprises the primary dataset and the selected feature; and train the model using the generated training dataset.

In another embodiment, a non-transitory computer-readable storage device storing instructions that, when executed by one or more processors, cause the one or more processors to: receive a secondary dataset to be combined with a primary dataset for generation of a training dataset, the primary dataset comprising a plurality of data records where at least one data record corresponds to a time-of-prediction value corresponding to a timestamp that at least one data record was used to generate a prediction, and the secondary dataset comprising a plurality of features where at least one feature corresponds to a timestamp value; select a feature within the secondary dataset with a timestamp that precedes or matches a time-of-prediction value for a corresponding data record within the primary dataset; generate the training dataset that comprises the primary dataset and the selected feature; and train the model using the generated training dataset.

BRIEF DESCRIPTION OF THE DRAWINGS

Advantages of the some embodiments may be understood by referring to the following description taken in conjunction with the accompanying drawings. In the drawings, like reference characters generally refer to the same parts throughout the different views. Also, the drawings are not necessarily to scale, emphasis instead generally being placed upon illustrating principles of some embodiments of the solution.

FIG. 1A illustrates execution steps for a model training and deployment system, in accordance with an embodiment.

FIGS. 1B-1D illustrate a graphical user interface depicting primary and secondary datasets, in accordance with different embodiments.

FIGS. 1E-1I illustrate relationships between primary and secondary datasets, in accordance with different embodiments.

FIG. 1J illustrates a graphical user interface depicting various time thresholds, in accordance with an embodiment.

FIGS. 1K-1N illustrate different time relationships between primary and secondary datasets.

FIG. 1O illustrates a graphical user interface depicting different categorical values within a training dataset, in accordance with an embodiment.

FIG. 1P illustrates a graphical user interface depicting coefficients of categorical values associated with a target, in accordance with an embodiment.

FIG. 2 illustrates a graphical user interface depicting a relationship between different features within a dataset, in accordance with an embodiment.

FIGS. 3A-3B illustrate a recipe, in accordance with an embodiment.

FIG. 4 illustrates a graphical user interface depicting a lineage map, in accordance with embodiment.

FIG. 5A illustrates a block diagram of embodiments of a computing device, in accordance with an embodiment.

FIG. 5B illustrates a block diagram depicting a computing environment that includes a client device in communication with a cloud service provider, in accordance with an embodiment.

FIG. 6 illustrates a block diagram of a predictive modeling system, in accordance with some embodiments, in accordance with an embodiment.

FIG. 7 illustrates a block diagram of a modeling tool for building machine-executable templates encoding predictive modeling tasks, techniques, and methodologies, in accordance with some embodiments, in accordance with an embodiment.

FIG. 8 illustrates a flowchart of a method for selecting a predictive model for a prediction problem, in accordance with some embodiments, in accordance with an embodiment.

FIG. 9 illustrates another flowchart of a method for selecting a predictive model for a prediction problem, in accordance with some embodiments, in accordance with an embodiment.

FIG. 10 illustrates a schematic of a predictive modeling system, in accordance with some embodiments, in accordance with an embodiment.

FIG. 11 illustrates another block diagram of a predictive modeling system, in accordance with some embodiments.

DETAILED DESCRIPTION

The present disclosure encompasses various methods to analyze data within different datasets, such that data can be extracted, manipulated, transformed into a training dataset. For purposes of reading the description of the various embodiments below, the following descriptions of the sections of the specification and their respective contents may be helpful:

Section A: This aspect of the disclosure encompasses various methods of generating a time-aware training dataset. Specifically, data records and features can be analyzed and various thresholds (e.g., feature derivation windows) can be manipulated, such that the training dataset does not include data that would cause target leakage for a machine learning model.

Section B: This aspect of the disclosure encompasses various methods of analyzing datasets to derive and uncover new features and hidden relationships.

Section C: This aspect of the disclosure encompasses various methods of allowing low latency prediction for a machine learning model.

Section D: This aspect of the disclosure encompasses various methods for generating and/or visualizing a lineage map between multiple datasets.

Section E: This aspect of the disclosure encompasses removing various features that are either non-informative or have a low predictive value to the machine learning model.

Section F: This aspect of the disclosure encompasses various methods of allowing low latency prediction for a machine learning model. This aspect of the disclosure can be performed in embodiments where features cannot be generated in advance,

Sections G-K. This aspect of the disclosure encompasses various embodiments for methods and computing environments in which the methods described herein can be implemented.

Section L. This aspect of the disclosure encompasses descriptions for various terminology and phraseology used herein.

A. Time-Aware Feature Engineering from Secondary Datasets

The systems and methods described herein may automatically generate useful features from multiple datasets (e.g., disparate or unconnected datasets) into a single training dataset for the purpose of building and/or training a machine learning model. The systems and methods are general enough to work on datasets for a wide variety of use cases, handle a variety of data types, and scale to a large number of datasets, which can have complex inter-relationships. By comparison, conventional data preparation tools (e.g., ALTERYX, TRIFACTA, and TALEND) have been found to provide unsatisfactory results. Such previous tools, for example, do not provide an adequate solution for machine learning. For instance, some conventional tools use an exhaustive strategy that takes too long and cannot scale, and others work on individual data sources but not a collection of datasets. The feature generation techniques described herein (referred to as supervised automated feature engineering and reduction (SAFER)) provides a solution to these technical shortcomings.

The systems and methods described herein (SAFER) can automatically decide what features to generate and/or take into account the time-of-prediction for each training example. This time-awareness aspect of the systems and methods described herein ensures that the machine learning model is not susceptible to target leakage. Target leakage is, in general, a significant mistake in which a machine learning model learns from data (e.g., a subset of the training data) that was not accessible at the time of prediction. This can cause the model to achieve overly optimistic performance during validation that is not reproduced in actual application. Such leakage can happen in many ways, but lack of time-awareness during feature engineering is a common culprit, which can be systematically avoided using the systems and methods described herein.

Primary Dataset and Relationships

The methods and systems described herein allow a model training and deployment system having one or more processors to generate a training dataset by adding data records and features extracted from one or more secondary datasets into a primary dataset. The one or more processors may also analyze and manipulate certain data records and thresholds, such as the training dataset is more efficiently generated. Once the training dataset is generated, the training dataset can be ingested by one or more models for training purposes.

In certain instances, the primary dataset is designated that can provide a structure or template for a new training dataset. The training dataset can be a single table with multiple rows and columns in which each row represents a single training example. The columns can include the objective or target (e.g., likelihood of loan default), variables that characterize the example (e.g., income, credit rating, loan amount, and the like), as well as an optional time-of-prediction. The time-of-prediction can be, for example, a date and/or time at which a prediction was (or should have been) made for the example. In a loan default example, the time-of-prediction can be a date or time when a loan application was submitted or received.

To guide the feature engineering process, the model training and deployment system can obtain or receive information about how secondary datasets are related to the primary dataset. Such information can specify, for example, a pair of datasets (e.g., the primary dataset and a secondary dataset) and a set of columns or join keys from each of the two datasets that define the relationship. The simplest type of relationship can be defined by an exact match between the join keys (e.g., a “user id” column in each dataset or table). However, the relationship can be generalized to more complex conditions such as, for example, spatial and/or temporal proximity. For example, a zip code column in one table can be linked to a city or town column in another table. The relationships between datasets can be defined manually, detected automatically (e.g., from database schema), or estimated using inclusive dependence algorithms.

Referring now to FIG. 1A, a flowchart depicting operational steps for the model training and deployment system is depicted, in accordance with an embodiment. The method 100 describes how a processor or a server of the model training and deployment system can analyze primary and secondary datasets and generate a training dataset accordingly. The method 100 can be executed by any server or processor depicted in FIGS. 5A-7 and 10-11. Other configurations of the method 100 may comprise additional or alternative steps, or may omit one or more steps altogether. Moreover, some of the steps of the method 100 may be executed by another processor or server (e.g., local processor on an electronic device) under direction and instructions from the model training and deployment system.

The method 100 allows the model training and deployment system to generate a training dataset. As described herein, each data record (or row) in that dataset is associated with one or more features. Using the method 100, a subset of the data records may be used to identify related records in a secondary dataset for feature engineering. Specifically, one or more features from this subset of rows in the secondary dataset can be used to generate a new feature in the primary dataset (as depicted in FIGS. 1E-1I).

In a non-limiting example, if one of the features is a timestamp that is designated as the time-of-prediction, the timestamp will be used to constrain the extraction of records from the secondary dataset for feature engineering. If the secondary dataset includes a timestamp feature designated as the time of record creation, records in the secondary dataset that are used to engineer features for each row in the primary dataset will be restricted to those where the time of record creation is within the feature derivation window determined by the time-of-prediction value of the primary record.

At step 110, the model training and deployment system may receive a secondary dataset to be combined with a primary dataset for generation of a training dataset, the primary dataset comprising a plurality of data records where at least one data record corresponds to a time-of-prediction value corresponding to a timestamp that at least one data record was used to generate a prediction, and the secondary dataset comprising a plurality of features where at least one feature corresponds to a timestamp value. As described herein, the model training and deployment system may analyze data within one or more secondary datasets (e.g., files and other data received from a user), extract appropriate data in light of various rules discussed herein, and generate a training dataset accordingly.

Referring now to FIG. 1B, in some implementations, relationships between datasets can be defined using a visual graph editor 101. The visual graph editor 101 can start with a single dataset (e.g., a primary dataset 111) on a canvas 104. A user can access a platform provided by the model training and deployment system that includes the visual graph editor 101. The user can then add one or more new secondary datasets (e.g., secondary dataset 106) by uploading a file, or selecting a dataset registered in a data catalog. Each secondary dataset that may be added to the canvas 104 can be connected directly or indirectly to the primary dataset 111 using the methods discussed herein. FIG. 1B shows a listing 108 of several available secondary datasets. The user can specify which secondary datasets to include in the project and can define how each secondary dataset relates to the primary dataset 111, for example, so that one or more columns or records from each secondary data set can be added to or combined with the primary dataset 111.

In a non-limiting example, the user can access the visual graph editor 101 to input secondary datasets depicted in the listing 108. The user may select data to be uploaded in the model training and deployment system, such that the data can be used to train one or more models. As will be described below, the data uploaded or otherwise inputted by the user may be reconfigured and re-engineered and features within the data added may be extracted and added to the primary dataset 111. The model training and deployment system may then train one or more models accordingly.

Referring to FIG. 1C, an example of the user defining join conditions between a pair of datasets using the visual graph editor 101 is depicted. In this example, records from the secondary dataset 112 (“employee_profile”) are related to records from the primary dataset 111 (“train”) through a matching “employee_id” column in the two datasets. In this non-limiting embodiment, “employee_id” serves as a join key, which can specify how records in one dataset relate to records in another dataset. FIG. 1D depicts an example of the visual graph editor 101 in which five secondary datasets 114 a-e (collectively 114) are linked to the primary dataset 111 through the “employee_id” join key.

Even though certain aspects of FIGS. 1B-1D depict a user interacting with a graphical user interface (e.g., visual graph editor 101) to input secondary datasets, it is expressly understood that inputting data can be accomplished through other methods, such as batch updates or other non-visual methods of transmitting data.

As described herein, the primary (or first) dataset may include data records where at least one data record corresponds to a time-of-prediction value. For instance, the data records may indicate data associated with a user (known historical data), such as personal, demographic, or financial data. Each data record may correspond to a timestamp that indicates a time of prediction value. For instance, and continuing with the example above, the primary dataset may indicate that when a decision or a prediction was made in relation to the user.

The secondary dataset may include a set of features. A feature of a data sample may be a measurable property of an entity (e.g., person, thing, event, activity, etc.) represented by or associated with the data sample. For example, a feature can be the price of an apartment. As a further example, a feature can be a shape extracted from an image of the apartment. In some cases, a feature of a data sample is a description of (or other information regarding) an entity represented by or associated with the data sample. A value of a feature may be a measurement of the corresponding property of an entity or an instance of information regarding an entity. For instance, in the above example in which a feature is the price of an apartment, a value of the feature can be $1,000. As referred to herein, a value of a feature can also refer to a missing value (e.g., no value). For instance, in the above example in which a feature is the price of an apartment, the price of the apartment can be missing.

Features can also have data types. For instance, a feature can have an image data type, a numerical data type, a text data type (e.g., a structured text data type or an unstructured (“free”) text data type), a categorical data type, or any other kind of data type. In some cases, the feature values for one or more features corresponding to a set of observations may be organized in a table, in which case those feature(s) may be referred to herein as “tabular features.” Features of the numerical data type and/or categorical data type are often tabular features. In the above example, the feature of a shape from an image of the apartment can be of an image data type. In general, a feature's data type is categorical if the set of values that can be assigned to the feature is finite.

Referring back to FIG. 1A, at step 120, the model training and deployment system may select a feature within the secondary dataset with a timestamp that precedes or matches a time-of-prediction value for a corresponding data record within the primary dataset. The model training and deployment system may analyze the data within the primary dataset and the secondary dataset. Using various analytical protocols discussed herein, the model training and deployment system may recognize, analyze, extract, and manipulate data into a single training dataset. In some configurations, the data may reside in multiple federated data repositories. However, the pertinent data necessary to accomplish efficient training may be linked, such that at training time or at time-of-prediction, the model can ingest the data necessary to train itself. As will be described below, at step 130, the model training and deployment system may generate the training dataset that comprises the primary dataset and the selected feature(s).

Column and Relationship Type Detection

The model training and deployment system may execute an automatic column type detection protocol to determine the nature or type of data in each column within a dataset. Column type can include, for example, general types such as numeric, categorical, text, or images, as well as specific subtypes such as integer, high cardinality categorical, low cardinality integer, and the like. Such characterization of columnar data can allow certain heuristics to be applied, as described herein, for generating features amenable to machine learning techniques. The heuristics can be derived from statistical inferences and/or can be used to determine type-specific feature generation strategies. To maximize performance from machine learning techniques, the generated features preferably include or satisfy a sufficient number of observations (e.g., above a threshold number, such as 100, 1000, or more), variance (e.g., non-constant values or sufficient correlation with the target), or other statistical characteristics. Some of these approaches may not scale well to the number of features (e.g., due to excessive computation times or data storage requirements) and hence strategies used may not be exhaustive but can be based on sensible heuristics.

To extract features from multiple datasets into a final training dataset, relationships between the datasets can be used to determine how data should be summarized, manipulated, and/or transferred from one dataset to another. Each dataset relationship can involve two datasets and the type of relationship can be characterized as either one-to-many (alternatively referred to as “collection”) or one-to-one (alternatively referred to as “lookup”). Columns can be transferred via lookup relationships, for example, using a left join or other suitable join operation from a source dataset (e.g., a secondary dataset) to the destination dataset (e.g., the primary dataset), while collections may require some form of summarization or aggregation (e.g., from multiple rows into a single row) before the transfer of columns or other dataset portions can occur.

Feature Discovery Process

After identifying data within different datasets, the model training and deployment system may execute one or more analytical protocols to identify features within the datasets. With columns and dataset relationships characterized, a recursive graph traversal algorithm (e.g., depth-first or breadth-first) can be used to explore each dataset, starting with the primary dataset. During the exploration process, candidate features can be generated using heuristics that take into consideration the nature of the columns as well as a type of relationship between datasets or that leads to a dataset. For example, the following pseudocode can be used to perform a feature exploration or discovery algorithm. The algorithm can include a depth-first search and a type-based transformation and aggregation.

 function discover features(table, path):   path.add(table)   // process each connected table for each relationship, related table in relationships(table):   if related_table in path:    continue   discover_features(related_table)   // add features from connected table to this table   if is_lookup(relationship):    join_features_from(related_table, table, relationship)   else:    agg_features_from(related_table, table, relationship)   // add transformed features to this table   add_transforms(table)   path.pop(table)

After identifying the data within the datasets, the model training and deployment system may extract certain features and combine data records to generate a training dataset that would decrease (or sometimes eliminate) target leakage. FIGS. 1F-1I illustrate an example in which six tables (a Primary Table 128E, Table 1A 130E, Table 1B 132E, Table 2A 134E, Table 2B 136E, and Table 2C 138E) are used to generate a training dataset. Referring now to FIG. 1E, the Primary Table 128E includes an initial set of features PT that provide a starting point or template for the training dataset. Table 1A 130E, Table 1B 132E, Table 2A 134E, Table 2B 136E, and Table 2C 138E include initial sets of features 1A, 1B, 2A, 2B, and 2C, respectively. Use of the term “Lookup” or the term “Collection” in these figures indicates whether the relationship between two tables is one-to-one or one-to-many, respectively.

In one example, a discover_features function (e.g., in the above pseudocode) can be called (e.g., for the Primary Table 128 or other current or destination table) and can recursively process all connected or related tables (e.g., Table 1A 130E, Table 1B 132E, Table 2A 134E, Table 2B 136E, and Table 2C 138E), for example, by iterating through the related tables and joining or aggregating features from the related table to the current or destination table (e.g., from Table 2A 134E to Table 1A 130E, or from Table 1A 130E to the Primary Table 128E). The relationship (e.g., one-to-one or one-to-many) between the two tables or features in the tables can be used to determine whether a join (lookup) or aggregation (collection) operation is performed. This process can be recursively applied to each related table.

The add_transforms function (e.g., in the above pseudocode) can be used to apply feature transformations on features in a table to generate new features (e.g., in the same table). The types of transformations to be applied can depend on the feature types (e.g., text, date, etc.). For example, a transformation on a date feature can be performed to extract a day of the week value as a new feature, which can be represented as an integer between 0 to 6. In FIGS. 1E-1I, “f” or “f(S)” represents a set of transformed features created by the add_transforms function.

The agg_features_from function (e.g., in the above pseudocode) can be used to apply feature aggregation on features from a related table (e.g., Table 2A 134E) to generate new features in a current or destination table (e.g., Table 1A 130E). The type of aggregation to be applied can depend on feature types. For example, numerical statistics such as minimum, maximum, or average can be applied to a numeric feature, while categorical statistics such as number of unique values, most frequent value, or entropy can be applicable for categorical features. In FIGS. 1E-1I, “g” or “g(S)” represents a set of aggregated features created by the agg_features_from function.

Referring to FIG. 1F, a set of transformed features f(2A) can be created in Table 2A 134F (e.g., using the add_transforms function) and/or the transformed features f(2A) can be added to Table 1A 130F. After performing these operations, Table 1A 130F includes features 1A and f(2A). The relationship between Table 1A 130F and Table 2A 134F in this example is one-to-one (lookup).

Referring to FIG. 1G, to combine features from Table 2B 136G with Table 1A 130G, a set of transformed features f(2B) can be created in Table 2B 136G (e.g., using the add_transforms function), and the transformed features can be aggregated to generate a set of aggregated features g(f(2B)) (e.g., using the agg_features_from function), which is then added to Table 1A 130G. After performing these operations, Table 1A 130G includes features 1A, f(2A), and g(f(2B)). The relationship between Table 1A 130G and Table 2B 136G in this example is one-to-many (collection).

Referring to FIG. 1H, a set of transformed features f(2C) can be created in Table 2C 138H (e.g., using the add_transforms function), and the transformed features f(2C) can be added to Table 1B 132H. After performing these operations, Table 1B includes features 1B and f(2C). The relationship between Table 1B 132H and Table 2C 138H in this example is one-to-one (lookup).

FIG. 1H also indicates that a set of transformed features f(1A+f(2A)+g(f(2B))) can be created in Table 1A 130H (e.g., using the add_transforms function) and the transformed features f(1A+f(2A)+g(f(2B))) can be added to the Primary Table 128H. After performing these operations, the Primary Table 128H includes features PT and f(1A+f(2A)+g(f(2B))). The relationship between the Primary Table 128H and Table 1A 130H in this example is one-to-one (lookup).

Referring to FIG. 1I, to combine features from Table 1B 132I with the Primary Table 128I, a set of transformed features f(1B+f(2C)) can be created in Table 1B 132I (e.g., using the add_transforms function), and the transformed features can be aggregated to generate a set of aggregated features g(f(1B+f(2C))) (e.g., using the agg_features_from function), which is then added to the Primary Table 128I. After performing these operations, the Primary Table 128I includes features PT, f(1A+f(2A)+g(f(2B))), and g(f(1B+f(2C))). The modified Primary Table 128I can be used as training data 115 for a machine learning model. The relationship between the Primary Table 128I and Table 1B 132I in this example is one-to-many (collection).

Time-Aware Feature Engineering

In general, for models that make predictions about the future, it is important to ensure that the model is not trained using data that would not be available when predictions are made (e.g., to avoid target leakage). To achieve this, the systems and methods described herein can include a time-of-prediction parameter or feature in the training data. The time-of-prediction can specify, for example, a time when a prediction is or was made for each example in the training data.

When features are added to the training data (e.g., using the transformation and/or aggregation operations described with respect to FIGS. 1E-1I), the features can be screened to ensure that they include only data that is or was available at the time-of-prediction, to avoid peeking into the future and causing target leakage. This screening process can be performed, for example, by knowing or considering a record creation time (e.g., time index) of each row or example in the datasets. For example, if a row was created after a corresponding time-of-prediction, then the features or data from the row can be ignored or not included in the training dataset. This approach of using time-of-prediction to avoid target leakage can be referred to as being “time-aware.”

As used herein, “time-of-prediction” can be or refer to a date or time column (e.g., in the primary dataset) that specifies a time when a prediction is or was made or needs to be made. Such a time may need to be considered to understand how the target is defined. For example, when the target is the likelihood of loan default three months after the loan application, the time-of-prediction can be when the loan application was submitted, received, or processed (e.g., three months before the default event is predicted to occur) and not when the default occurred.

In various examples, a “time-index” can be or refer to a date or time column (e.g., in a secondary dataset) that specifies a date or time of the event represented by each row. For example, the time-index can indicate that an event (e.g., a change in income) represented by a row occurred on a certain date, and this can be used to determine if the event occurred before or after the time-of-prediction (e.g., a date of the loan application).

A “feature derivation window” can be or refer to a time range or period before the time-of-prediction within which data can be used to generate features. The feature derivation window can be used, for example, to ensure features are obtained or generated for each example using historical data that transpired within the same amount of time and/or with the same lag relative to a moment of making a prediction. There can be a gap between the end of this window and the time-of-prediction, in some instances, and there can be practical constraints in obtaining data associated with the gap, for example, due to a delay in data collection. The gap can be referred to as a “blind history gap” or BHG. One way to estimate the size of the BHG in a database table is to measure or determine a delay between the time of the event and the record creation time in each row. The entire disclosure of U.S. Pat. No. 10,496,927, “Systems for Time-Series Predictive Data Analytics, and Related Methods and Apparatus,” Attorney Docket No. DRB-002ACP, is incorporated by reference in its entirety.

Referring to FIG. 1J, in some instances, a user interface 116 can be used to manually add or revise a specified and inputted feature derivation window. In the depicted example, a user specified that only historical records having a record date falling within a feature derivation window from 30 days to 1 day prior to the prediction point (time-of-prediction) can be used for feature derivation. Accordingly, the model training and deployment system may only consider this inputted time as the feature derivation window and perform the calculations described herein.

In certain implementations, use of a different feature derivation window for each unique time-of-prediction can result in significantly increased computation complexity. To simplify the approach, time-of-prediction can be rounded (e.g., rounded down to a nearest hour or day) to reduce a number of unique feature derivation windows to be used. For example, the user interface 116 in FIG. 1J allows the user to specify an amount of rounding for the time-of-prediction, for example, to the nearest minute, hour, day, or week.

In certain embodiments, the model training and deployment system may analyze timestamps associated with different data records and features stored within the primary and/or secondary dataset(s). As a result, the system can determine which features and data records to include within the training dataset.

FIGS. 1K-1N illustrate examples in which time-of-prediction is taken into consideration when features or data is being obtained from a secondary table. Each figure depicts a portion of a primary table 118 (e.g., training data) and a portion of records from a secondary table 121. Each example in the primary table portion 118 includes a time-of-prediction, and each record in the secondary table portion 121 includes an event timestamp or time of creation for the record. As the figures illustrate, various records in the secondary table portion 121 can be considered and/or ignored for feature extraction depending on the time-of-prediction values in the primary table portion 118, for example, to ensure that data unavailable at prediction time is not used or considered.

For example, referring to FIG. 1K, when the time-of-prediction is 12:34 pm on 2016 Nov. 1, three of the four records in the secondary table 121 precede the time-of-prediction and can be considered or added to the primary table 118, as indicated by the “3” in the No. of Records column.

Likewise, referring now to FIG. 1L, the same three out of four records can be considered or added to the primary table when the time-of-prediction is 2:21 pm on 2016 Nov. 1. Referring to FIG. 1M, when the time-of-prediction is 6:02 pm on 2016 Nov. 1, all four records precede the time-of-prediction and can be considered or added to the primary table, as indicated by the “4” in the No. of Records column. In general, this time-aware approach of extracting features from secondary tables may require feature extraction to be done separately for each distinct time-of-prediction value in the primary table or dataset.

Referring to FIG. 1N, to improve computational efficiency, the time-of-prediction values in the primary table can be rounded down to the nearest day (or hour or minute, as appropriate), so that all three examples in the primary table have the same time-of-prediction. While this may sacrifice a little accuracy by using slightly outdated features for some examples, rounding to the nearest day in this case results in only one distinct time-of-prediction (e.g., 00:00:00 on 2016 Nov. 1), such that feature extraction from the secondary dataset can be done all at once (rather than separately for each time-of-prediction value). In this case, three of the four records precede the single time-of-prediction and can be considered or added to each example in the primary table, as indicated by “3” in the No. of Records column.

This rounding of time-of-prediction can be applied depending on the nature of the secondary datasets. For example, if the time-interval between records in the secondary table is one month (e.g., one record per month), then the end result may be same whether time-of-prediction is rounded to the nearest minute or the nearest day, because both options may result in the same set of records being available for feature extraction. Additionally or alternatively, the rounding can be more aggressive when the window is large (e.g., 10 years vs. 10 days) and/or when the target being predicted is less sensitive to the recency of the data.

Scalable Data Integration and Human Readable Feature Engineering Recipes

In some implementations, the systems and methods described herein can utilize a scalable, distributed computation framework, such as APACHE SPARK, to integrate data from multiple data sources, which can be or include static files or dynamic database tables. Ingested sources can be registered in context and manipulated via Structured Query Language (SQL) (e.g., specific to APACHE SPARK). In certain examples, the systems and methods utilize a human-readable representation of a feature engineering recipe that can be converted into SQL. The human-readable representation can be more amenable to audit and troubleshooting.

As used herein, a recipe refers to a method, workflow, or any other analytical protocol that identifies and/or captures the elements chosen to best address a problem or a task. For instance, a recipe can be code that determines what data should and should not be used (e.g., analyzed or otherwise ingested) by a model to accomplish a task (e.g., output results). A recipe may be model agnostic, such that the same recipe (e.g., code) can be re-used for more efficient results.

For example, feature engineering recipes can be represented in a format that can be easily serialized to JSON text for portability. Additionally or alternatively, each table can contain information about the columns in the table and/or how the columns were derived, which can facilitate the process of deriving a lineage for each column (e.g., in the primary table), as described herein.

In one example, a primary table has two columns “employee[salary]” and “employee[gender]” that can be derived from a left join to or with an “employee” table. Example code (e.g., a full recipe) for performing this operation is as follows:

# primary table  “primary_table”: { “columns”: {   “employee[salary]”:    { “definition”: {     “inputs”: [[“employee”, “salary”]],     “join”: {      “join_type”: “left”,      “left_keys”: [“employee_id”],      “right_keys”: [“employee_id”],     }    }   },    “employee[gender]”: {    “definition”: {    “inputs”: [[“employee”, “gender”]],    “join”: {      “join_type”: “left”,      “left_keys”: [“employee_id”],      “right_keys”: [“employee_id”],      }     }    }   }  } }, # employee table  “employee”: {   “columns”: {    “salary”: {     “definition”: {      “inputs”: [[“employee”, “salary”]],      “join”: null     }    },    “gender”: {     “definition”: {     “inputs”: [[“employee”, “gender”]],     ”join”: null    }   }  } }

In certain implementations, to simplify computational complexity and/or data transfer, the feature engineering recipe can be trimmed such that a reduced version generates only a subset of features. For example, to remove the “employee[gender]” column from the primary table, in addition to trimming the column from the primary table, the lineage of the column can be checked recursively to remove other columns (or corresponding datasets) used to derive “employee[gender]” column. In this case, for example, the “gender” column in the “employee” table can be removed, to arrive at the following simplified code (e.g., a trimmed recipe).

# primary table  “primary_table”: {   “columns”: {   “employee[salary]”: {    “definition”: {     “inputs”: [[“employee”, “salary”]],     “join”: {      “join_type”: “left”,      “left_keys”: [“employee_id”],      “right_keys”: [“employee_id”],    }   }  },  } }  # employee table   “employee”: {    “columns”: {     “salary”: {      “definition”: {       “inputs”: [[“employee”, “salary”]],       “join”: null       }      },   }  } }

The trimmed recipe can be used to generate an optimized SQL statement and also potentially remove the need to load some datasets that are no longer required. This capability has various practical applications such as, for example, deploying a simpler model that is less expensive to productionize and/or incrementally generating subsets of features at different times or with different computing resources. In general, when a column in the primary table (e.g., training data) is not needed, the feature lineage for that column can be processed to identify precursor columns that may also not be needed, and such columns and/or corresponding datasets can be removed or ignored.

In various implementations, for example, a recursive algorithm can be used to generate SQL statements (e.g., SPARK SQL statements) from a recipe formatted in a JSON structure, as shown above. Starting with the primary table, metadata for each column can be parsed to identify one or more joins that may be needed to derive the column. For example, the “employee[salary]” column in the example above was generated using a join from the “employee” table. The algorithm can then parse the metadata for any dependent tables (e.g., the “employee” table) to determine any joins used to generate columns in the dependent tables. This can be done recursively until there are no more dependent tables to consider. The algorithm can then generate one or more SQL statements for each table using metadata for the columns in the table, which can include, for example, metadata describing details related to input columns, operations, column names, and joins (e.g., if the input columns reside in another table). The generated SQL statements can provide a view of the table and/or describe how columns in the table were generated. One or more SQL statements can be used to represent the table as a nested SQL statement.

For example, the recursive algorithm can be used to generate the following SQL statement from the example full recipe, described above (prior to being trimmed).

SELECT employee.salary, employee.gender, FROM primary_table LEFT JOIN (  SELECT   employee.salary,   employee.gender,  FROM employee ) AS employee ON primary_table.employee_id = employee.employee_id

Likewise, the recursive algorithm can be used to generate the following SQL statement from the example trimmed recipe, described above.

SELECT  employee.salary FROM primary_table LEFT JOIN (  SELECT   employee.salary,  FROM employee ) AS employee ON primary_table.employee_id = employee.employee_id

Summarized Categorical (Categorical and Text Summarization)

The numeric features (e.g., from multiple rows) can be aggregated to obtain a derived or secondary numeric feature, such as minimum, maximum, and/or average, and categorical and/or text features can be aggregated to obtain summarized features, such as the number of words or the most frequent categorical. While such summarized features may not capture the rich amount of information present in the original data, not aggregating categorical or text features (e.g., capturing data for each unique categorical value) can result in an excessive number of features and/or increase computational complexity.

To address these challenges associated with categorical or text features, the systems and methods described herein can utilize a sparse representation of the occurrence of categorical features. The sparse representation can be, include, or utilize a JSON dictionary that captures the count of categorical values or text tokens observed in the data. The dictionary can be converted into sparse matrices during model building for efficient memory usage and computation using algorithms that handle sparse data. Sparse representations may store only non-zero values in memory, which can substantially reduce memory requirements.

In one example, a summarized categorical value representing the quantities and types of fruit types purchased by a customer at a supermarket during the last three months can be represented in a JSON string as follows: {“apple”: 15, “orange”: 10, “avocado”: 15, “banana”: 8}. If the supermarket sells 200 different fruit varieties, storing the customer's purchase information in a conventional tabular format may require 200 numeric columns, as shown in Table 1.

TABLE 1 Conventional, non-sparse tabular format for storing data. apple avocado pear grapefruit . . . 15 15 0 0 . . .

The JSON strings may need to be converted to sparse matrices, such that they can be ingested by one or more machine learning models. Various formats for sparse matrices can be used, including the coordinate list (COO) format, which can represent the above purchase information as [(0, 15), (1, 15), (25, 10), (127, 8)], where 0, 1, 25 and 127 are column indices for the fruit types (e.g., 0=“apple”, 1=“avocado”, 25=“orange,” and 127=“banana”). Compared to a dense matrix requiring 200 integer values, the COO representation uses only 8 integer values, which translates to a memory reduction of 25 times. Moreover, matrix libraries that work with sparse matrices can be used to optimize common operations such as, for example, matrix multiplication and element-wise arithmetic, so that the computation times associated with such operations can be reduced significantly. The computational efficiency of working with sparse representations can depend on the sparseness of the values and may be beneficial only in certain instances, for example, when the data is sufficiently sparse. Referring now to FIG. 1O, a graphical user interface 122 depicts representations of various categorical values within a training dataset.

Referring now to FIG. 1P, a graphical user interface 124 depicts linear coefficients of categorical values associated with a target. The graphical user interface 124 presents words 126 representing the most relevant categorical values occurring in a column or other portion of a dataset. The words 126 can be color-coded (e.g., by font color intensity) indicating a degree to which the presence of the categorical value has a positive effect (e.g., red) or a negative effect (e.g., blue) on the target value. Additionally or alternatively, a font size for the words 126 can be used to indicate a frequency at which the categorical values appear in the data, for example, with more frequent categorical values being displayed in a larger font size.

Time-Specific Features

For time-aware feature engineering, various useful features can be generated relative to the time-of-prediction. Some of these features can include, for example, a time difference between the time-of-prediction and a previous recorded event (e.g., a most recent event), an entropy of binned recency of events (e.g., a measurement of how events are distributed over time within the feature derivation window, such as evenly distributed over the entire window or concentrated to a small time period), date related features (e.g., day-of-week, hour-of-day, or day-of-month), statistics of intervals between transactions or events (e.g., max, mean, or min), value of a feature in a most recent transaction, ratio or difference between computed features using two different feature derivation windows (e.g., as described herein), or other features.

B. Feature Interactions Detection

In certain implementations, the systems and methods described herein can be used to automatically identify and derive new features from one or more pairs of input features. The derived features can be used to uncover hidden relationships between the derived features and the target, thereby enabling more insights to be generated from user datasets. For example, referring to FIG. 2, a difference between a year when a house was sold and a year when the house was built can be derived as a new feature (e.g., “age of house when sold”), which can be an important model input for predicting a price of the house. FIG. 2 includes a graphical user interface 200 including a histogram 202 for date sold, a histogram 204 for date built, and a histogram 206 for age of house when sold.

Compared to previous approaches, the systems and methods described herein for identifying and deriving new features utilize novel algorithms and are less computationally expensive, especially when the number of features is large. More importantly, the systems and methods are able to focus on finding feature interactions that make sense to users.

In various implementations, for example, the systems and methods described herein can incorporate or use logic that focuses on increasing an interpretability of derived features, and/or can use heuristics to limit the search space to only simpler interactions, for example, that a user can explain without too much effort. Additionally or alternatively, a feature reduction layer can be included before searching through pairwise interactions to make the exploration process faster while still identifying and retaining more impactful interactions.

In certain examples, automatic interactions detection can include the following steps. First, the training dataset can be sampled or sub-sampled, for example, to identify possible combinations of features within the training dataset. Next, pre-feature reduction or removal can be performed. If there are too many features, for example, an elastic net model (or other suitable regression model or other model) can be built and/or feature impact from the trained model can be used to select a top subset of features on which interactions detection will be applied. Feature impact for a feature can be, for example, a measure of how sensitive a model's predictions are on that feature and/or how much the feature improves model accuracy.

Next, certain heuristics can be applied to verify interpretability and/or accuracy for each pair of selected features. For example, for a pair of features to have a suitable interpretability (e.g., easy for users to understand), the two features in the pair should have a high correlation coefficient with one another, preferably higher than a threshold value, such as 0.5 or 0.6. For example, plant growth may be highly correlated to weather conditions but not highly correlated to stock market performance. Additionally or alternatively, a pair of features (or a new feature derived from the pair) can have a suitable accuracy when the pair of features improves model accuracy.

Additionally or alternatively, for a difference between features to be sufficiently meaningful and/or interpretable, when fitting a generalized linear model, a pair should have coefficients that are opposite in sign and/or have a similar or same scale. In some instances, for a pair of features A and B, a generalized linear model can be fit in which a target y=aA+bB, to obtain coefficients a and b for features A and B, respectively. This pair of features A and B can be considered to pass an interpretability check when (i) a product of a and b (i.e., a*b) is negative (e.g., coefficients have opposite signs) and (ii) an absolute value of a ratio of a and b (e.g., |a/b|) falls within a desired range (e.g., coefficients have similar scales). For example, if a=1 and b=−4, the absolute value of the coefficient ratio can be 1/4, which may be less than a minimum acceptable threshold of 0.5, such that a and b may be considered to have different scales. In some instances, when two features have considerably different scales (e.g., one is in millimeters and the other in kilometers), the scale of one or both features can be adjusted to make the scales similar or identical (e.g., so both are in meters).

Alternatively or additionally, for a ratio of two features to be sufficiently meaningful and/or interpretable, both features should have the same sign (e.g., both should be positive or both should be negative), and/or the pair should not have a divide by zero issue (e.g., the feature in a denominator should not be zero). When searching for possible ratios, if a pair of features has a divide by zero issue, the pair may fail the interpretability check. Alternatively or additionally, to preserve useful information that may otherwise be present in a ratio, data points having a divide by zero issue can be treated as missing values.

In some instances, an equal flag can be used to indicate whether two features are equal. For the equal flag to be sufficiently meaningful and/or interpretable, the pair should have a sufficient number of unique values (e.g., on a percentage basis or greater than a threshold value of 10, 100, or more).

To verify accuracy for numeric interactions (e.g., features derived from numeric features), a generalized linear model can be fit with a derived feature, and the significance of the derived feature can be determined, for example, using p-values or other statistical criteria. Additionally or alternatively, to verify accuracy for text interactions (e.g., features derived from text features), a generalized linear model can be fit with a derived feature (e.g., using a cosine similarity score on tokenized text) and the significance of the derived feature can be determined, for example, using p-values or other statistical criteria. In general, an interaction between a pair of features may be considered to be useful or valid only when the interaction satisfies both interpretability (e.g., easy for a user to understand) and accuracy (e.g., use of the derived feature improves model accuracy).

In some examples, a post-feature reduction procedure can be performed when too many interaction features (e.g., derived feature candidates) are found. The feature reduction procedure can involve, for example, building an elastic net model (or other suitable regression model) on all interaction features and using feature impact from the model to select the most impactful interactions or feature combinations. In general, the goal of the feature reduction procedure is to identify features having the highest feature impact (e.g., correlation with the target). This feature reduction procedure can be performed during or immediately following the detection of feature interactions or feature derivation. Additionally or alternatively, the feature reduction procedure can be performed instead of or in addition to other feature reduction procedures described herein.

C. Feature Cache

In various examples, the systems and methods described herein can provide support for low latency predictions for a machine learning model that uses features engineered from multiple datasets, such as features derived automatically from other features, as described herein. Because features can be derived from or involve multiple datasets, which can reside in various forms (e.g., files, database tables, value stores, etc.), substantial computation and/or data transfer operations may be required to create features when making predictions.

To support low latency predictions, the systems and methods described herein can reduce computation and/or data transfer operations by pre-caching one or more features (e.g., previously calculated features), SQL statements or code used to generate features, names of features that may be generated, join keys that may be used to join a primary table with generated features, a lookup table storing recently generated features and/or names for such features, building blocks for features, hash tables, and/or other information that can be used to derive engineered features when needed (e.g., when model predictions are being made). Generating and using feature caches (alternatively referred to as feature stores) can involve, for example, identifying one or more cached tables required for a given feature engineering recipe, generating or performing separate recipes to populate cache tables with a certain schedule, and reassembling features, as needed, for a given prediction request.

While previous open source frameworks (e.g., HOPWORKS and FEAST) may have certain feature store capabilities, such solutions may provide functionality only for collaborative creation and maintenance of a single, wide dataset with versioning capabilities. Each row in the dataset can represent a certain entity (e.g., a user, a product, a vehicle, etc.), and each column can be a feature that characterizes the entity in some way (e.g., age, price, depreciation rate, etc.). These solutions can allow multiple users to craft their own features and share the features with others so that a subset of the collective features can be used when crafting a data science solution, such as building a machine learning model. Such datasets can be used to populate features for training a model or making a prediction. Accordingly, such previous solutions may provide only a basic solution for data collection and may rely on users to ensure data is provided with adequate quality and recency. It may not be convenient or possible with such previous solutions to build time-aware training data that involves examples with different time-of-prediction values, which is important when building a machine learning model that makes prediction into the future, as described herein.

By comparison, the systems and methods described herein can integrate with SAFER solution, described herein, and can automatically populate feature caches from multiple data sources, for example, according to a schedule that can be simulated in the training data for consistency. For example, the schedule can define a frequency at which the feature cache is refreshed, such as every hour or every day. Delays in refreshing the feature cache can cause the feature cache to be slightly outdated when used, which may introduce certain inaccuracies or errors when calculating features. Such delays can be simulated or taken into consideration when generating features for the training dataset, for example, so that training data features and new prediction features (e.g., generated with feature cache) are consistent. Feature cache manipulation can be built into a model deployment process, such that human intervention may not be required for the creation, maintenance, and/or decommissioning of data tables or datasets.

In some instances, the feature cache approach can be tightly integrated with systems and methods described herein. For example, feature recipes determined from SAFER can utilize the feature cache approach to generate engineered features quickly and efficiently (e.g., from multiple data sources), when needed. The feature engineering solution can create features automatically, for example, from the integration of multiple datasets, and can support generation of features in a time-aware fashion that enables predictive modeling, as described herein. To support real-time generation of such features for prediction, the features or components or building blocks thereof can be pre-populated in a manner (e.g., using feature caches) that is fast enough to access when needed and scalable enough to handle relatively large datasets.

In certain examples, the feature cache approach described herein can include: creating an optimized query (e.g., a SPARK SQL query) for each cache table, automatically updating the cache table values with the latest datasets, and efficiently and quickly querying the feature values. To perform feature cache creation, a feature cache table can store a full or partial set of features generated from an SAFER recipe. When a model that uses the SAFER recipe is deployed, the features used by the model can be identified. Given that the model may use only a subset of the features to generate predictions, a pruned SAFER recipe can be produced that instantiates only the subset of features. This can avoid unnecessary computation associated with generating features that are not used by the model. As datasets or portions thereof used in the recipe can be joined to a primary dataset using join keys, separate cache tables can be created to store features specific to each of the joined datasets or features. Each cache table can include a row for each possible value in the set of join keys.

For example, referring to FIG. 3A, a feature engineering recipe can create two features “table_a[value]” and “table_b[value]” in a primary table 300 by joining features from table_a 302 and table_b 304. To generate the table_a[value] and table_b[value] features in this example, the following SQL statement can be run to perform join operations between the primary table and table_a 302 and table_b 306, as follows:

SELECT  aid,  bid,  table_a[value]  table_b[value] FROM primary_table LEFT JOIN table_a ON (table_a.id = primary_table.aid) LEFT JOIN table_b ON (table_b.id = primary_table.bid)

In this case, the two features “table_a[value]” and “table_b[value]” can be looked up in table_a and table_b, respectively, using keys “aid” and “bid”. Accordingly, to facilitate calculations, two feature stores can be created in this example: one (Feature Cache A) for features looked up using “aid” and another (Feature Cache B) for features looked up using “bid.” Features that are present in the primary table generally do not need to be included in the feature stores given that such features may already exist and/or can be part of the example in a prediction request. For example, feature stores can be used to generate features that do not already reside in the primary table but instead are calculated from other data sources, for example, when predictions are made. In some instances, a feature cache can be used to store information (e.g., SQL statements, features, feature building blocks, lookup tables, hash maps or tables, and the like) used to generate features quickly or in real-time, as needed. In various implementations, a feature cache can or should be created and/or used whenever low latency predictions are desired or required.

Feature Cache Update

In certain examples, each feature cache can be populated using a trimmed SQL recipe that generates only features that belong to the feature cache. Each recipe can include all the possible values of join keys that may be used to lookup features. For the example above, the SQL recipe for Feature Cache A may be as follows:

SELECT  aid,  table_a[value] FROM (  SELECT DISTINCT id AS aid  FROM table_a ) AS primary_table LEFT JOIN table_a ON (table_a.id = primary_table.aid)

Similarly the SQL recipe for Feature Cache B may be as follows:

SELECT  bid,  table_b[value] FROM (  SELECT DISTINCT id AS bid  FROM table _b ) AS primary_table LEFT JOIN table_b ON (table_b.id = primary_table.bid)

Referring to FIG. 3B, these SQL recipes can populate Feature Cache A 304 and Feature Cache B 306, which can be used as lookup tables for the respective features (e.g., table_a[value] and table_b[value]), for example, based on join keys in the primary table. This can facilitate lookup operations during prediction time, for example, because the model or user may initially have access to only the primary table, and the primary table may include join keys but not the features stored in the cache tables (e.g., Feature Cache A 306 and Feature Cache B 308). The example shown in FIGS. 3A-3B is a simple example used for illustrative purposes. In other examples, a recipe to populate the cache can be or include a nested SQL with arbitrary complexity, for example, with features in table_a 302 and table_b 304 derived from other tables, and so on.

In various implementations, a scheduling frequency of updates (e.g., for a feature cache) can have a direct impact on how up to date the feature values are, and consequently can impact prediction accuracy. To assess the expected performance of a model that makes predictions using a feature cache, features can be generated for the training data in a similar fashion. For each example in the training data, for example, the effective time-of-prediction used to perform time-aware feature engineering can be rounded down to the time of the last scheduled feature update. This adjustment can be automated given that the feature engineering process is automated. Cross validation of models built using different scheduling frequencies can be used to assess the impact scheduling frequency has on accuracy. Such information can be useful when choosing an update schedule for specific use cases.

Feature Cache Lookup

In a typical example, during prediction time, the model and or user may have access only to certain features or join keys present in the primary table (e.g., aid and bid) and certain additional features may need to be reconstructed. For example, to reconstruct the table_a[value] and table_b[value] features for prediction, the following SQL query can be made on the feature cache database.

SELECT  aid,  bid,  fs_a.table_a[value], fs_b.table_b[value] FROM primary_table LEFT JOIN fs_a ON (feature cache_a.aid = primary_table.aid) LEFT JOIN fs_b ON (feature cache_b.bid = primary_table.bid)

For example, still referring to 3B, when a new prediction request is made with {“aid”: 3, “bid”: z}, a lookup can be performed in Feature Cache A 306 and Feature Cache B 308 to determine that the corresponding values for features table_a[value] and table_b[value] are “pear” and “bear,” respectively.

{ “aid”: 3, “bid”: z, “fs_a.table_a[value]”: “pear”, “fs_a.table_b[value]”: “bear” }

Most relational databases can support such queries in reasonable time for real-time operations. Such databases can store data on disk or in memory using indexes on join keys for efficient retrieval. In the example above, indexes can be created for aid in Feature Cache A 306 and bid in Feature Cache B 308.

D. Feature Lineage

In various examples, the systems and methods described herein can generate images or diagrams of feature recipes or lineage that can allow users to more easily visualize and understand how features are generated. Such visualizations can be useful for auditing and risk management, and/or for gaining insights from machine learning models. Because feature recipes can be complex (e.g., involving multiple joins to filter relevant rows, grouping, aggregation, and/or transformation), determining feature lineage and/or providing such diagrams be challenging and, in some instances, can involving imaging tens or hundreds of lines of SQL, or more.

Conventional approaches for determining or visualizing feature lineage can require a user to create or execute a series of steps on one or more datasets, which can include transforming values on one or more columns, joining two tables, aggregating a column after grouping by another, etc.

Advantageously, by comparison, the systems and methods described herein can utilize an automated workflow for determining feature lineage. Additionally or alternatively, the systems and methods can be tailored for the automated feature engineering solution, described herein, and can extract an individual workflow or lineage for each generated feature (e.g., a single feature) from a full workflow used to produce all the features. To extract an individual workflow for a generated feature, input features (e.g., immediate precursor or parent features) for the generated feature can be inspected to identify any operations (e.g., aggregations and/or transformations) used to generate the input features (e.g., from earlier precursor or parent features). This inspection process can be applied recursively until original parent features (e.g., from one or more secondary datasets) are reached. The information derived from this process can be or represent a lineage corresponding to the generated feature, which can identify the original parent features, any intermediate parent features, and operations (e.g., aggregations or transformations) used to derive the generated feature.

FIG. 4 includes a graphical user interface of a diagram of a feature lineage chart for a feature derivation recipe. In the depicted example, a final node indicates that a generated feature 402 is referred to as “Diagnosis[DiagnosisDescription] (tokens).” A panel 404 (e.g., labeled “Original”) can identify initial datasets and/or initial or precursor features used to obtain the generated feature 402. In this case, the panel 404 indicates that the two initial datasets used to derive the generated feature are “Diagnosis” and “Primary dataset,” and the initial features are “PatientGuid” and “DiagnosisDescription” in the Diagnosis dataset and “PatientGuid” in the Primary dataset. The diagram also indicates that a series of operations or actions (e.g., “Map of value counts” and “Value counts”) are performed on the initial DiagnosisDescription feature to generate an intermediate feature. The intermediate feature is then joined into the primary dataset (e.g., using PatientGuid).

The generated feature 402 can then be encoded into JSON format (e.g., using a “Value counts to JSON” operation), if desired, which may allow the feature to be recognized as a summarized categorical feature by the systems and methods described herein. The generated feature 402 derived in this manner can be used for modeling.

In some examples, feature lineage for a derived feature (e.g., the generated feature 402) can be obtained by working backwards (from the derived feature) through a structured JSON recipe (or SQL commands) until original datasets are reached. For example, a JSON recipe can be parsed recursively in a manner similar to how JSON recipes can be parsed to generate SQL statements, as described herein. Alternatively or additionally, feature lineage information can be obtained from feature engineering recipes, as described herein, that encode the steps used to generate each feature. For example, each step in a recipe for a generated feature can be processed to identify initial datasets, initial features, intermediate features, and/or any feature operations. The systems and methods can process the feature operations to determine the input and output features for each operation, which can be used to determine a complete lineage for the generated feature. The lineage can be presented in a feature lineage chart or diagram (e.g., with nodes and edges) showing an information flow and/or steps used to derive the generated feature, for example, as shown in FIG. 4.

E. Feature Reduction

In certain implementations, the systems and methods described herein can automatically remove features that (i) are non-informative from a modeling perspective and/or (ii) have low predictive value based on the user-selected target. This process of removing features (e.g., to reduce a total number of features) that are not helpful or useful can ensure that a final set of features provides good prediction accuracy, within a reasonable autopilot runtime.

Compared to existing strategies for feature reduction, which can be based on overly simplified checks (e.g., for features having more than two unique values) and/or univariate statistical testing, the feature reduction approach described herein can utilize a complete model blueprint to evaluate the usefulness of candidate features. The model blueprint can be or include, for example, a description of a sequence of data processing, feature engineering, algorithm training, algorithm tuning, and other steps for implementing and using the model. Use of the model blueprint can ensure that the feature evaluation process automatically benefits from expertise in data type detection, data preprocessing, hyperparameters tuning, and/or model explanation, such as feature impact. Before applying a model blueprint, checks can be applied that can detect redundant features even if such features are not exact duplicates of each other.

In certain examples, the initial feature engineering recipe generated by the systems and methods described herein can encode the recipe for all possible derived features that may make sense or be desired, for example, for a given a set of datasets or relationships among datasets. In some cases, however, many of the possible derived features may not be useful when applied to actual datasets. The recipe may therefore be applied on a sample or subset of the data. Two main feature reduction approaches can be applied to a set of features: unsupervised feature reduction and supervised feature reduction.

Unsupervised feature reduction can involve removing features that (i) are all missing values, (ii) are constant, and/or (iii) are exact duplicates of one or more other features. Additionally or alternatively, features that represent or include duplicate information present in one or more other features (e.g., day of the week can be represented as “Monday”, “Tuesday”, . . . or “1”, “2”, . . . ) can be detected as duplicate and removed. Such detection can be performed, for example, by ordinally encoding all categorical features. Additionally or alternatively, features that require too much memory for storage and/or are too big in terms of on-disk size can be removed. Such a data size limit can be used to ensure downstream modeling stages are able to perform well without running out of memory. For example, when the output data size limit is set to 2 GB, a candidate feature having a size of 2.2 GB (e.g., due to frequently long texts) may be removed.

Supervised feature reduction can involve, for example, training a tree-based model blueprint on a set of materialized features and calculating feature impact based on the trained model. The feature impact can be or represent, for example, a Shapley-based feature importance, which can be derived by training a model using the features and calculating average absolute Shapley values for each feature. The Shapley value may indicate an importance (e.g., overall contribution, level of contribution, or amount of contribution) of each feature within the overall prediction of a machine learning model. For example, the Shapley value may indicate the amount of contribution of a feature value across possible coalitions. For instance, the Shapley value may be an average (sometimes weighted average) of the marginal contributions for all features.

Each feature can then be given a quality score based on the resulting feature impact values. In some examples, the quality score for each feature can be equal to the feature impact value for that feature. Alternatively or additionally, the quality score can be further adjusted based on a correlation between the feature and one or more other features. When a correlation between two features exceeds a predefined threshold, for example, the quality score for one or both of the features can be discounted or reduced (e.g., by 10%, 25%, 50%, or more).

Reducing quality scores in this manner can prevent multiple correlated features from receiving high quality scores and/or from being retained in the final set of features. In general, features having low quality scores can be removed and features having high quality scores can be retained. Features can be retained, for example, when a sum of the quality scores for the features exceeds a high proportion of a total of the quality scores (e.g. 99%). For example, features (e.g., derived features) having the highest quality scores and that contribute to some threshold amount of the total of the quality scores (e.g., 80%, 90%, 95%, 97%, or 99%) can be retained, while other features with lower quality scores can be removed.

Referring back to FIG. 1A, at step 140, the model training and deployment system may train a model using the generated training dataset. The model training and deployment system may use a variety of training methods to train one or more models using the generated training dataset. “Machine learning” generally refers to the application of certain techniques (e.g., pattern recognition and/or statistical inference techniques) by computer systems to perform specific tasks. Machine learning systems may build predictive models based on sample data (e.g., “training data”) and may validate the models using validation data (e.g., “testing data”). The sample and validation data may be organized as sets of records (e.g., “observations”), with each record indicating values of specified data fields (e.g., “dependent variables,” “outputs,” or “targets”) based on the values of other data fields (e.g., “independent variables,” “inputs,” “features,” or “predictors”). When presented with other data (e.g., “scoring data”) similar to or related to the sample data, the machine learning system may use such a predictive model to accurately predict the unknown values of the targets of the scoring data set.

After a predictive problem is identified, the process of using machine learning to build a predictive model that accurately solves the prediction problem generally includes steps of data collection, data cleaning, feature engineering, model generation, and model deployment. “Automated machine learning” techniques may be used to automate steps of the machine learning process or portions thereof.

A machine-learning model can be an unsupervised machine learning model or a supervised machine learning model. Unsupervised and supervised machine learning models differ from one another based on their training datasets and algorithms. Specifically, a training dataset used to train an unsupervised machine learning model generally does not include target values for the individual training samples, while a training dataset used to train a supervised machine learning model generally does include target values for the individual training samples. The value of a target for a training sample may indicate a known classification of the training sample or a known value of an output variable of the training sample. For example, a target for a training sample used to train a supervised computer vision model to detect images containing a cat can be an indication of whether or not the training sample includes an image containing a cat.

Following training, a machine learning model is configured to generate predictions based on a scoring dataset. Targets are generally not known in advance for samples in a test dataset, and therefore a machine learning model generates predictions for the test dataset based on prior training. For example, following training, a computer vision model may be configured to distinguish data samples including images of cats from data samples that do not include images of cats.

In contrast to development of a machine learning model, as referred to herein, the term “deployment” with regard to a machine learning model may refer to use of a developed machine learning model to generate real-world predictions. A deployed machine learning model may have completed development (e.g., training). A model can be deployed in any system, including the system in which it was developed and/or a third-party system. A deployed machine learning model can make real-world predictions based on a scoring data set. Unlike certain embodiments of a training data set, scoring data set generally does not include known outcomes. Rather, the deployed machine learning model is used to generate predictions of outcomes based on the scoring data set.

Using the methods and systems described herein, the model training and deployment system may train one or more machine learning models using a training dataset generated via executing (at least partially) the method 100. For instance, a machine learning model may ingest the training dataset and train itself accordingly.

Even though the present disclosure described the model training and deployment system as actively training one or more machine learning models, in some configurations, the system may transmit the training dataset to a third-party server or computing environment, such that the third party server can train the machine learning model. In another example, the training and deployment system may use the methods discussed herein to deploy a model. For instance, the system may use the feature generation methods discussed herein to generate a dataset that is then analyzed/ingested by a model to predict results.

F. Low Latency Feature Discovery for Inference

As described above, the model training and deployment system may use various caching methods discussed in Subsection C to provide low latency prediction for users. Additionally or alternatively, the model training and deployment system may provide support for low latency prediction for the deployed model(s) using passed/inputted datasets. Using this method, the model training and deployment system can support real-time prediction where datasets (including secondary datasets) change often. Additionally or alternatively, in some embodiments, the secondary datasets are only known to the system (e.g., received) during prediction time. As a result, pre-computing features using the feature caching method may not be possible or efficient. Therefore, the system may execute this method (described in Subsection F) that provides low latency prediction instead of feature cache method discussed in Subsection C.

In those instances, the model training and deployment system may be required to execute various feature generation protocols discussed herein in real-time using existing secondary datasets or passed/inputted datasets. For example, due to the real-time (or near real-time) requirement for the model training and deployment system to produce results, the system may be required to generate features from a secondary dataset in real-time (or near real-time). In order to achieve this, the system may use the method described in subsection F. The methods described in subsection F may be executed in conjunction with other methods described in any other subsection.

Conventional low latency prediction protocols may not apply to some (or all) of the feature generation methods described herein. For instance, conventional and existing low latency prediction protocols may not apply to a system that also supports an automated feature engineering methods and systems. Instead, the model training and deployment system may utilize a method derived from the caching mechanism, which allows re-using datasets when multiple prediction requests are made. Specifically, the feature generation method during prediction request may utilize cached dataset(s) and existing session(s) to reduce the computational requirements and cost.

As discussed above, in some implementations, the systems and methods described herein can utilize a scalable, distributed computation framework, such as APACHE SPARK, to integrate data from multiple data sources, which can be or include static files or dynamic database tables. Ingested sources can be registered in context and manipulated via Structured Query Language (SQL) (e.g., specific to APACHE SPARK). Using this method, the model training and deployment system may generate a session (or a SPARK session) for an instance of execution of a particular machine learning model. As will be described below, the system may use data associated with the session to provide faster (low latency) predictions for subsequent predictions.

The method described herein may be utilized for machine learning models that are deployed for a particular user. The system may pre-process the data, such that the data is available for the machine learning model at time-of-prediction. This pre-processing may include creating a reduced SQL (e.g., SPARK SQL) that includes information about secondary datasets used by the machine learning model.

A SPARK SQL may be a blueprint/pipeline to generate all the project features. The reduced SPARK SQL may be derived from the full SPARK SQL and may contain information regarding how to generate features that are required by a given deployed model. Reducing the SQL code may simplify computational complexity and/or data transfer, such that the reduced version generates only a subset of features that is needed. Therefore, the model training and deployment system can produce more efficient results. In this way, the system avoids creating unnecessary extra features, which are not used by the model. For the reusability purposes the reduced code may be saved into the file. For instance, after a model is deployed, the reduced code may be saved in a data repository that can be accessed the next time the model is deployed to predict results.

When a prediction request is received, the model training and deployment system may provide the user with the option of inputting/identifying secondary datasets or foregoing (skipping) inputting secondary datasets. If the datasets are inputted, then the inputted datasets are used for feature generation via various methods discussed herein. Otherwise, default datasets specified at deployment creation can be used instead. Specifically, the system may utilize an existing session (SPARK session) that includes preloaded datasets for feature generation. Using the features generated, the system may execute the model and output predictions for the user.

The low latency prediction method discussed herein may use a dedicated session (e.g., SPARK session) created for a given deployed model to generate features for a subsequent prediction. The dedicated session may be cached for reusability. Cached sessions may be preloaded (or otherwise associated) with the secondary datasets used by the deployed model. When facing multiple sessions, the system may selectively implement the method on a subset of the sessions. For instance, the system may not account for the least recently used (LRU) sessions or the least frequently used (LFU) sessions. For instance, when needed, the system may remove the LRU or LFU sessions and/or the data associated with the LRU and LFU sessions.

While a model producing prediction results for a first request may take longer, the low latency prediction method described herein may provide prediction for a subsequent request in less time (e.g., 5 seconds). This is because the system may use pre-processed data associated with the existing session (e.g., session associated with the first prediction). To determine a timeout status for the execution of the model, the system may use/analyze data associated with a feature generation frequency saved within one or more cached data tables.

To further reduce time needed to execute the model and output a prediction, the system may not perform exploratory data analysis (EDA) on the datasets used, such as datasets received/passed by the user. EDA, as used herein, refers to various methods and analytical protocols used to analyze and investigate datasets and/or to summarize their main characteristics. While EDA identifies how best to utilize a dataset or manipulate/analyze the data records associated with the dataset, EDA may be a time-consuming process that may negatively impact a low latency prediction. Therefore, the model training and deployment system may either forego EDA entirely or perform a reduced version of EDA when attempting to provide low latency prediction. For instant, instead of running full analysis on the datasets (e.g., datasets inputted by the user), the system may only execute limited data verification to avoid system failure or to identify flaws within the dataset (e.g., missing values, missing or incorrect column names, and/or incorrect data type).

In order to provide low latency prediction, the model training and deployment system may provide a limit on the datasets received (e.g., inputted by the user). For instance, the number of rows used by a model may be limited to reduce computational time and resources, for example, to 100 rows within a scoring dataset. In those embodiments, before executing the model (before datasets are ingested and analyzed by the model), the system may verify that the dataset complies with the 100 row threshold. If the dataset does not comply, the system may not perform the low latency prediction methods described herein.

Non-Limiting Example

A server associated with the model training and deployment system disclosed herein may use the methods discussed herein to train and deploy a machine learning model. Specifically, the server may train the machine learning model using data that is specific to an end user. The model training and deployment system may provide an electronic platform (e.g., the visual graph editor 101) that allows an end user to upload one or more datasets that is specific to the end user and/or their entity.

Using the electronic platform, the end user may upload data (e.g., files, image data, raw data, spreadsheets, and the like), such that the data is accessible to the server. The server then analyzes the data using various methods disclosed within the Section A of the present disclosure. Specifically, the server may analyze timestamps associated with the data records/features within the inputted data and may only select a subset of the data that would not cause target leakage (e.g., would not violate time-of-prediction thresholds). The server may accomplish this by analyzing timestamps associated with different features and/or revising feature derivation windows. The server may identify relationships between the features within the secondary dataset (e.g., data inputted by the end user) and data records within a primary dataset (e.g., dataset providing structure to the training dataset). For instance, the server may execute various protocols to identify the type of data stored within the columns of the secondary datasets and execute a feature discovery process to identify, summarize, and engineer meaningful features to be included within the training dataset.

The server may optionally execute a feature interaction detection protocol (described in Section B) to derive new features from one or more pairs of input features. The newly derived features may be included within the training dataset. Using various training methods, the server may train one or more machine learning models, such that the training is customized and focused on the data inputted by the end user.

Because the secondary dataset may be voluminous (e.g., include a high number of features), the server may execute a feature reduction protocol, as described in Section F. The server may optionally generate a linage map, as described in Section D. The lineage map may indicate how the secondary dataset related to the primary dataset and/or the training dataset. The server may display a visual representation of the lineage map on the electronic platform, such as depicted in FIG. 4.

When deploying the machine learning model (e.g., to produce predictions for the end user), the server may optionally cache various portions of the data, such that the machine learning model can provide low latency results. This process is described in Section C.

G. Computing Environment

FIGS. 5A-5B depict example computing environments that form, perform, or otherwise provide or facilitate systems and methods of epidemiological modeling using machine learning. FIG. 5A illustrates an example computing device 500, which can include one or more processors 505, volatile memory 610 (e.g., random access memory (RAM)), non-volatile memory 520 (e.g., one or more hard disk drives (HDDs) or other magnetic or optical storage media, one or more solid state drives (SSDs) such as a flash drive or other solid state storage media, one or more hybrid magnetic and solid state drives, and/or one or more virtual storage volumes, such as a cloud storage, or a combination of such physical storage volumes and virtual storage volumes or arrays thereof), user interface (UI) 525, one or more communications interfaces 515, and communication bus 530. User interface 525 may include graphical user interface (GUI) 550 (e.g., a touchscreen, a display, etc.) and one or more input/output (I/O) devices 555 (e.g., a mouse, a keyboard, a microphone, one or more speakers, one or more cameras, one or more biometric scanners, one or more environmental sensors, one or more accelerometers, etc.).

Non-volatile memory 520 can store the operating system 535, one or more applications 540, and data 545 such that, for example, computer instructions of operating system 535 and/or applications 540 are executed by processor(s) 505 out of volatile memory 510. In some embodiments, volatile memory 510 may include one or more types of RAM and/or a cache memory that may offer a faster response time than a main memory. Data may be entered using an input device of GUI 650 or received from I/O device(s) 555. Various elements of computing device 500 may communicate via one or more communication buses, shown as communication bus 530.

Clients, servers, and other components or devices on a network can be implemented by any computing or processing environment and with any type of machine or set of machines that may have suitable hardware and/or software capable of operating as described herein. Processor(s) 505 may be implemented by one or more programmable processors to execute one or more executable instructions, such as a computer program, to perform the functions of the system. As used herein, the term “processor” describes circuitry that performs a function, an operation, or a sequence of operations. The function, operation, or sequence of operations may be hard coded into the circuitry or soft coded by way of instructions held in a memory device and executed by the circuitry. A “processor” may perform the function, operation, or sequence of operations using digital values and/or using analog signals. In some embodiments, the “processor” can be embodied in one or more application specific integrated circuits (ASICs), microprocessors, digital signal processors (DSPs), graphics processing units (GPUs), microcontrollers, field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), multi-core processors, or general-purpose computers with associated memory. The “processor” may be analog, digital or mixed-signal. In some embodiments, the “processor” may be one or more physical processors or one or more “virtual” (e.g., remotely located or “cloud”) processors. A processor including multiple processor cores and/or multiple processors multiple processors may provide functionality for parallel, simultaneous execution of instructions or for parallel, simultaneous execution of one instruction on more than one piece of data.

Communications interfaces 515 may include one or more interfaces to enable computing device 500 to access a computer network such as a Local Area Network (LAN), a Wide Area Network (WAN), a Personal Area Network (PAN), or the Internet through a variety of wired and/or wireless or cellular connections.

The computing device 500 may execute an application on behalf of a user of a client computing device. The computing device 500 can provide virtualization features, including, for example, hosting a virtual machine. The computing device 500 may also execute a terminal services session to provide a hosted desktop environment. The computing device 500 may provide access to a computing environment including one or more of: one or more applications, one or more desktop applications, and one or more desktop sessions in which one or more applications may execute.

FIG. 5B depicts an example computing environment 560. Computing environment 560 may generally be considered implemented as a cloud computing environment, an on-premises (“on-prem”) computing environment, or a hybrid computing environment including one or more on-prem computing environments and one or more cloud computing environments. When implemented as a cloud computing environment, also referred as a cloud environment, cloud computing or cloud network, computing environment 560 can provide the delivery of shared services (e.g., computer services) and shared resources (e.g., computer resources) to multiple users. For example, the computing environment 560 can include an environment or system for providing or delivering access to a plurality of shared services and resources to a plurality of users through the internet. The shared resources and services can include, but not limited to, networks, network bandwidth, servers 595, processing, memory, storage, applications, virtual machines, databases, software, hardware, analytics, and intelligence.

In embodiments, the computing environment 560 may provide clients 565 with one or more resources provided by a network environment. The computing environment 560 may include one or more clients 565, in communication with a cloud 575 over a network 570. The cloud 575 may include back end platforms, e.g., servers 595, storage, server farms or data centers. The clients 565 can include one or more component or functionality of computing device 500 depicted in FIG. 5A.

The users or clients 565 can correspond to a single organization or multiple organizations. For example, the computing environment 560 can include a private cloud serving a single organization (e.g., enterprise cloud). The computing environment 560 can include a community cloud or public cloud serving multiple organizations. In embodiments, the computing environment 560 can include a hybrid cloud that is a combination of a public cloud and a private cloud. For example, the cloud 575 may be public, private, or hybrid. Public clouds 575 may include public servers 595 that are maintained by third parties to the clients 565 or the owners of the clients 565. The servers 195 may be located off-site in remote geographical locations as disclosed above or otherwise. Public clouds 575 may be connected to the servers 195 over a public network 570. Private clouds 575 may include private servers 195 that are physically maintained by clients 565 or owners of clients 565. Private clouds 575 may be connected to the servers 195 over a private network 570. Hybrid clouds 575 may include both the private and public networks 670 and servers 195.

The cloud 575 may include back end platforms, e.g., servers 195, storage, server farms or data centers. For example, the cloud 575 can include or correspond to a server 195 or system remote from one or more clients 565 to provide third party control over a pool of shared services and resources. The computing environment 560 can provide resource pooling to serve multiple users via clients 565 through a multi-tenant environment or multi-tenant model with different physical and virtual resources dynamically assigned and reassigned responsive to different demands within the respective environment. The multi-tenant environment can include a system or architecture that can provide a single instance of software, an application or a software application to serve multiple users.

In some embodiments, the computing environment 560 can include and provide different types of cloud computing services. For example, the computing environment 560 can include Infrastructure as a service (IaaS). The computing environment 560 can include Platform as a service (PaaS). The computing environment 560 can include server-less computing. The computing environment 560 can include Software as a service (SaaS). For example, the cloud 575 may also include a cloud based delivery, e.g. Software as a Service (SaaS) 580, Platform as a Service (PaaS) 585, and Infrastructure as a Service (IaaS) 590. IaaS may refer to a user renting the use of infrastructure resources that are needed during a specified time period. IaaS providers may offer storage, networking, servers or virtualization resources from large pools, allowing the users to quickly scale up by accessing more resources as needed. PaaS providers may offer functionality provided by IaaS, including, e.g., storage, networking, servers or virtualization, as well as additional resources such as, e.g., the operating system, middleware, or runtime resources. SaaS providers may offer the resources that PaaS provides, including storage, networking, servers, virtualization, operating system, middleware, or runtime resources. In some embodiments, SaaS providers may offer additional resources including, e.g., data and application resources.

Clients 565 may access IaaS resources with one or more IaaS standards. Some IaaS standards may allow clients access to resources over HTTP, and may use Representational State Transfer (REST) protocol or Simple Object Access Protocol (SOAP). Clients 565 may access PaaS resources with different PaaS interfaces. Some PaaS interfaces use HTTP packages, standard Java APIs, JavaMail API, Java Data Objects (JDO), Java Persistence API (JPA), Python APIs, web integration APIs for different programming languages including, e.g., Rack for Ruby, WSGI for Python, or PSGI for Perl, or other APIs that may be built on REST, HTTP, XML, or other protocols. Clients 565 may access SaaS resources through the use of web-based user interfaces, provided by a web browser. Clients 565 may also access SaaS resources through smartphone or tablet applications. Clients 565 may also access SaaS resources through the client operating system.

In some embodiments, access to IaaS, PaaS, or SaaS resources may be authenticated. For example, a server or authentication server may authenticate a user via security certificates, HTTPS, or API keys. API keys may include various encryption standards such as, e.g., Advanced Encryption Standard (AES). Data resources may be sent over Transport Layer Security (TLS) or Secure Sockets Layer (SSL).

H. Predictive Modeling System

Prior to discussing embodiments of epidemiologic modeling using machine learning, an overview of a predictive modeling system is provided. A predictive modeling system for use Data analysts can use analytic techniques and computational infrastructures to build predictive models from electronic data, including operations and evaluation data. Data analysts generally use one of two approaches to build predictive models. With the first approach, an organization dealing with a prediction problem simply uses a packaged predictive modeling solution already developed for the same prediction problem or a similar prediction problem. This “cookie cutter” approach, though inexpensive, is generally viable only for a small number of prediction problems (e.g., fraud detection, churn management, marketing response, etc.) that are common to a relatively large number of organizations. With the second approach, a team of data analysts builds a customized predictive modeling solution for a prediction problem. This “artisanal” approach is generally expensive and time-consuming, and therefore tends to be used for a small number of high-value prediction problems.

The space of potential predictive modeling solutions for a prediction problem is generally large and complex. Statistical learning techniques are influenced by many academic traditions (e.g., mathematics, statistics, physics, engineering, economics, sociology, biology, medicine, artificial intelligence, data mining, etc.) and by applications in many areas of commerce (e.g., finance, insurance, retail, manufacturing, healthcare, etc.). Consequently, there are many different predictive modeling algorithms, which may have many variants and/or tuning parameters, as well as different pre-processing and post-processing steps with their own variants and/or parameters. The volume of potential predictive modeling solutions (e.g., combinations of pre-processing steps, modeling algorithms, and post-processing steps) is already quite large and is increasing rapidly as researchers develop new techniques.

Given this vast space of predictive modeling techniques, some approaches, such as the artisinal approach, to generating predictive models tend to be time-consuming and to leave large portions of the modeling search space unexplored. Analysts tend to explore the modeling space in an ad hoc fashion, based on their intuition or previous experience and on extensive trial-and-error testing. They may not pursue some potentially useful avenues of exploration or adjust their searches properly in response to the results of their initial efforts. Furthermore, the scope of the trial-and-error testing tends to be limited by constraints on the analysts' time, such that the artisanal approach generally explores only a small portion of the modeling search space.

The artisanal approach can also be very expensive. Developing a predictive model via the artisanal approach often entails a substantial investment in computing resources and in well-paid data analysts. In view of these substantial costs, organizations often forego the artisanal approach in favor of the cookie cutter approach, which can be less expensive, but tends to explore only a small portion of this vast predictive modeling space (e.g., a portion of the modeling space that is expected, a priori, to contain acceptable solutions to a specified prediction problem). The cookie cutter approach can generate predictive models that perform poorly relative to unexplored options.

Thus, systems and methods of this technical solution can systematically and cost-effectively evaluate the space of potential predictive modeling techniques for prediction problems. This technical solution can utilize statistical learning techniques to systematically and cost-effectively evaluate the space of potential predictive modeling solutions for prediction problems.

Referring to FIG. 6, in some embodiments a predictive modeling system 600 includes a predictive modeling exploration engine 610, a user interface 620, a library 630 of predictive modeling techniques, and a predictive model deployment engine 240. The system 600 and its components can include one or more component or functionality depicted in FIGS. 5A-5B. The exploration engine 610 may implement a search technique (or “modeling methodology”) for efficiently exploring the predictive modeling search space (e.g., potential combinations of pre-processing steps, modeling algorithms, and post-processing steps) to generate a predictive modeling solution suitable for a specified prediction problem. The search technique may include an initial evaluation of which predictive modeling techniques are likely to provide suitable solutions for the prediction problem. In some embodiments, the search technique includes an incremental evaluation of the search space (e.g., using increasing fractions of a dataset), and a consistent comparison of the suitability of different modeling solutions for the prediction problem (e.g., using consistent metrics). In some embodiments, the search technique adapts based on results of prior searches, which can improve the effectiveness of the search technique over time.

The exploration engine 610 may use the library 230 of modeling techniques to evaluate potential modeling solutions in the search space. In some embodiments, the modeling technique library 630 includes machine-executable templates encoding complete modeling techniques. A machine-executable template may include one or more predictive modeling algorithms. In some embodiments, the modeling algorithms included in a template may be related in some way. For example, the modeling algorithms may be variants of the same modeling algorithm or members of a family of modeling algorithms. In some embodiments, a machine-executable template further includes one or more pre-processing and/or post-processing steps suitable for use with the template's algorithm(s). The algorithm(s), pre-processing steps, and/or post-processing steps may be parameterized. A machine-executable template may be applied to a user dataset to generate potential predictive modeling solutions for the prediction problem represented by the dataset.

The exploration engine 610 may uses the computational resources of a distributed computing system to explore the search space or portions thereof. In some embodiments, the exploration engine 610 generates a search plan for efficiently executing the search using the resources of the distributed computing system, and the distributed computing system executes the search in accordance with the search plan. The distributed computing system may provide interfaces that facilitate the evaluation of predictive modeling solutions in accordance with the search plan, including, without limitation, interfaces for queuing and monitoring of predictive modeling techniques, for virtualization of the computing system's resources, for accessing databases, for partitioning the search plan and allocating the computing system's resources to evaluation of modeling techniques, for collecting and organizing execution results, for accepting user input, etc.

The user interface 620 provides tools for monitoring and/or guiding the search of the predictive modeling space. These tools may provide insight into a prediction problem's dataset (e.g., by highlighting problematic variables in the dataset, identifying relationships between variables in the dataset, etc.), and/or insight into the results of the search. In some embodiments, data analysts may use the interface to guide the search, e.g., by specifying the metrics to be used to evaluate and compare modeling solutions, by specifying the criteria for recognizing a suitable modeling solution, etc. Thus, the user interface may be used by analysts to improve their own productivity, and/or to improve the performance of the exploration engine 610. In some embodiments, user interface 620 presents the results of the search in real-time, and permits users to guide the search (e.g., to adjust the scope of the search or the allocation of resources among the evaluations of different modeling solutions) in real-time. In some embodiments, user interface 620 provides tools for coordinating the efforts of multiple data analysts working on the same prediction problem and/or related prediction problems.

In some embodiments, the user interface 620 provides tools for developing machine-executable templates for the library 630 of modeling techniques. System users may use these tools to modify existing templates, to create new templates, or to remove templates from the library 630. In this way, system users may update the library 630 to reflect advances in predictive modeling research, and/or to include proprietary predictive modeling techniques.

The model deployment engine 640 provides tools for deploying predictive models in operational environments (e.g., predictive models generated by exploration engine 610). In some embodiments, the model deployment engine also provides tools for monitoring and/or updating predictive models. System users may use the deployment engine 640 to deploy predictive models generated by exploration engine 610, to monitor the performance of such predictive models, and to update such models (e.g., based on new data or advancements in predictive modeling techniques). In some embodiments, exploration engine 610 may use data collected and/or generated by deployment engine 640 (e.g., based on results of monitoring the performance of deployed predictive models) to guide the exploration of a search space for a prediction problem (e.g., to re-fit or tune a predictive model in response to changes in the underlying dataset for the prediction problem).

The system can include a library of modeling techniques. Library 630 of predictive modeling techniques includes machine-executable templates encoding complete predictive modeling techniques. In some embodiments, a machine-executable template includes one or more predictive modeling algorithms, zero or more pre-processing steps suitable for use with the algorithm(s), and zero or more post-processing steps suitable for use with the algorithm(s). The algorithm(s), pre-processing steps, and/or post-processing steps may be parameterized. A machine-executable template may be applied to a dataset to generate potential predictive modeling solutions for the prediction problem represented by the dataset.

A template may encode, for machine execution, pre-processing steps, model-fitting steps, and/or post-processing steps suitable for use with the template's predictive modeling algorithm(s). Examples of pre-processing steps include, without limitation, imputing missing values, feature engineering (e.g., one-hot encoding, splines, text mining, etc.), feature selection (e.g., dropping uninformative features, dropping highly correlated features, replacing original features by top principal components, etc.). Examples of model-fitting steps include, without limitation, algorithm selection, parameter estimation, hyper-parameter tuning, scoring, diagnostics, etc. Examples of post-processing steps include, without limitation, calibration of predictions, censoring, blending, etc.

In some embodiments, a machine-executable template includes metadata describing attributes of the predictive modeling technique encoded by the template. The metadata may indicate one or more data processing techniques that the template can perform as part of a predictive modeling solution (e.g., in a pre-processing step, in a post-processing step, or in a step of predictive modeling algorithm). These data processing techniques may include, without limitation, text mining, feature normalization, dimension reduction, or other suitable data processing techniques. Alternatively or in addition, the metadata may indicate one or more data processing constraints imposed by the predictive modeling technique encoded by the template, including, without limitation, constraints on dimensionality of the dataset, characteristics of the prediction problem's target(s), and/or characteristics of the prediction problem's feature(s).

In some embodiments, a template's metadata includes information relevant to estimating how well the corresponding modeling technique will work for a given dataset. For example, a template's metadata may indicate how well the corresponding modeling technique is expected to perform on datasets having particular characteristics, including, without limitation, wide datasets, tall datasets, sparse datasets, dense datasets, datasets that do or do not include text, datasets that include variables of various data types (e.g., numerical, ordinal, categorical, interpreted (e.g., date, time, text), etc.), datasets that include variables with various statistical properties (e.g., statistical properties relating to the variable's missing values, cardinality, distribution, etc.), etc. As another example, a template's metadata may indicate how well the corresponding modeling technique is expected to perform for a prediction problem involving target variables of a particular type. In some embodiments, a template's metadata indicates the corresponding modeling technique's expected performance in terms of one or more performance metrics (e.g., objective functions).

In some embodiments, a template's metadata includes characterizations of the processing steps implemented by the corresponding modeling technique, including, without limitation, the processing steps' allowed data type(s), structure, and/or dimensionality.

In some embodiments, a template's metadata includes data indicative of the results (actual or expected) of applying the predictive modeling technique represented by the template to one or more prediction problems and/or datasets. The results of applying a predictive modeling technique to a prediction problem or dataset may include, without limitation, the accuracy with which predictive models generated by the predictive modeling technique predict the target(s) of the prediction problem or dataset, the rank of accuracy of the predictive models generated by the predictive modeling technique (relative to other predictive modeling techniques) for the prediction problem or dataset, a score representing the utility of using the predictive modeling technique to generate a predictive model for the prediction problem or dataset (e.g., the value produced by the predictive model for an objective function), etc.

The data indicative of the results of applying a predictive modeling technique to a prediction problem or dataset may be provided by exploration engine 610 (e.g., based on the results of previous attempts to use the predictive modeling technique for the prediction problem or the dataset), provided by a user (e.g., based on the user's expertise), and/or obtained from any other suitable source. In some embodiments, exploration engine 610 updates such data based, at least in part, on the relationship between actual outcomes of instances of a prediction problem and the outcomes predicted by a predictive model generated via the predictive modeling technique.

In some embodiments, a template's metadata describes characteristics of the corresponding modeling technique relevant to estimating how efficiently the modeling technique will execute on a distributed computing infrastructure. For example, a template's metadata may indicate the processing resources needed to train and/or test the modeling technique on a dataset of a given size, the effect on resource consumption of the number of cross-validation folds and the number of points searched in the hyper-parameter space, the intrinsic parallelization of the processing steps performed by the modeling technique, etc.

In some embodiments, the library 630 of modeling techniques includes tools for assessing the similarities (or differences) between predictive modeling techniques. Such tools may express the similarity between two predictive modeling techniques as a score (e.g., on a predetermined scale), a classification (e.g., “highly similar”, “somewhat similar”, “somewhat dissimilar”, “highly dissimilar”), a binary determination (e.g., “similar” or “not similar”), etc. Such tools may determine the similarity between two predictive modeling techniques based on the processing steps that are common to the modeling techniques, based on the data indicative of the results of applying the two predictive modeling techniques to the same or similar prediction problems, etc. For example, given two predictive modeling techniques that have a large number (or high percentage) of their processing steps in common and/or yield similar results when applied to similar prediction problems, the tools may assign the modeling techniques a high similarity score or classify the modeling techniques as “highly similar”.

In some embodiments, the modeling techniques may be assigned to families of modeling techniques. The familial classifications of the modeling techniques may be assigned by a user (e.g., based on intuition and experience), assigned by a machine-learning classifier (e.g., based on processing steps common to the modeling techniques, data indicative of the results of applying different modeling techniques to the same or similar problems, etc.), or obtained from another suitable source. The tools for assessing the similarities between predictive modeling techniques may rely on the familial classifications to assess the similarity between two modeling techniques. In some embodiments, the tool may treat all modeling techniques in the same family as “similar” and treat any modeling techniques in different families as “not similar”. In some embodiments, the familial classifications of the modeling techniques may be just one factor in the tool's assessment of the similarity between modeling techniques.

In some embodiments, predictive modeling system 700 includes a library of prediction problems (not shown in FIG. 7). The library of prediction problems may include data indicative of the characteristics of prediction problems. In some embodiments, the data indicative of the characteristics of prediction problems includes data indicative of characteristics of datasets representing the prediction problem. Characteristics of a dataset may include, without limitation, the dataset's width, height, sparseness, or density; the number of targets and/or features in the dataset, the data types of the data set's variables (e.g., numerical, ordinal, categorical, or interpreted (e.g., date, time, text, etc.); the ranges of the dataset's numerical variables; the number of classes for the dataset's ordinal and categorical variables; etc.

In some embodiments, characteristics of a dataset include statistical properties of the dataset's variables, including, without limitation, the number of total observations; the number of unique values for each variable across observations; the number of missing values of each variable across observations; the presence and extent of outliers and inliers; the properties of the distribution of each variable's values or class membership; cardinality of the variables; etc. In some embodiments, characteristics of a dataset include relationships (e.g., statistical relationships) between the dataset's variables, including, without limitation, the joint distributions of groups of variables; the variable importance of one or more features to one or more targets (e.g., the extent of correlation between feature and target variables); the statistical relationships between two or more features (e.g., the extent of multicollinearity between two features); etc.

In some embodiments, the data indicative of the characteristics of the prediction problems includes data indicative of the subject matter of the prediction problem (e.g., finance, insurance, defense, e-commerce, retail, internet-based advertising, internet-based recommendation engines, etc.); the provenance of the variables (e.g., whether each variable was acquired directly from automated instrumentation, from human recording of automated instrumentation, from human measurement, from written human response, from verbal human response, etc.); the existence and performance of known predictive modeling solutions for the prediction problem; etc.

In some embodiments, predictive modeling tool 700 may support time-series prediction problems (e.g., uni-dimensional or multi-dimensional time-series prediction problems). For time-series prediction problems, the objective is generally to predict future values of the targets as a function of prior observations of all features, including the targets themselves. The data indicative of the characteristics of a prediction problem may accommodate time-series prediction problems by indicating whether the prediction problem is a time-series prediction problem, and by identifying the time measurement variable in datasets corresponding to time-series prediction problems.

In some embodiments, the library of prediction problems includes tools for assessing the similarities (or differences) between prediction problems. Such tools may express the similarity between two prediction problems as a score (e.g., on a predetermined scale), a classification (e.g., “highly similar”, “somewhat similar”, “somewhat dissimilar”, “highly dissimilar”), a binary determination (e.g., “similar” or “not similar”), etc. Such tools may determine the similarity between two prediction problems based on the data indicative of the characteristics of the prediction problems, based on data indicative of the results of applying the same or similar predictive modeling techniques to the prediction problems, etc. For example, given two prediction problems represented by datasets that have a large number (or high percentage) of characteristics in common and/or are susceptible to the same or similar predictive modeling techniques, the tools may assign the prediction problems a high similarity score or classify the prediction problems as “highly similar”.

FIG. 7 illustrates a block diagram of a modeling tool 700 suitable for building machine-executable templates encoding predictive modeling techniques and for integrating such templates into predictive modeling methodologies, in accordance with some embodiments. User interface 620 may provide an interface to modeling tool 700.

In the example of FIG. 7, a modeling methodology builder 310 builds a library 712 of modeling methodologies on top of a library 630 of modeling techniques. A modeling technique builder 720 builds the library 630 of modeling techniques on top of a library 732 of modeling tasks. A modeling methodology may correspond to one or more analysts' intuition about and experience of what modeling techniques work well in which circumstances, and/or may leverage results of the application of modeling techniques to previous prediction problems to guide exploration of the modeling search space for a prediction problem. A modeling technique may correspond to a step-by-step recipe for applying a specific modeling algorithm. A modeling task may correspond to a processing step within a modeling technique.

In some embodiments, a modeling technique may include a hierarchy of tasks. For example, a top-level “text mining” task may include sub-tasks for (a) creating a document-term matrix and (b) ranking terms and dropping terms that may be unimportant or that are not to be weighted or considered as highly. In turn, the “term ranking and dropping” sub-task may include sub-tasks for (b.1) building a ranking model and (b.2) using term ranks to drop columns from a document-term matrix. Such hierarchies may have arbitrary depth.

In the example of FIG. 7, modeling tool 700 includes a modeling task builder 730, a modeling technique builder 720, and a modeling methodology builder 310. Each builder may include a tool or set of tools for encoding one of the modeling elements in a machine-executable format. Each builder may permit users to modify an existing modeling element or create a new modeling element. To construct a complete library of modeling elements across the modeling layers illustrated in FIG. 7, developers may employ a top-down, bottom-up, inside-out, outside-in, or combination strategy. However, from the perspective of logical dependency, leaf-level tasks are the smallest modeling elements, so FIG. 7 depicts task creation as the first step in the process of constructing machine-executable templates.

Each builder's user interface may be implemented using, without limitation, a collection of specialized routines in a standard programming language, a formal grammar designed specifically for the purpose of encoding that builder's elements, a rich user interface for abstractly specifying the desired execution flow, etc. However, the logical structure of the operations allowed at each layer is independent of any particular interface.

When creating modeling tasks at the leaf level in the hierarchy, modeling tool 700 may permit developers to incorporate software components from other sources. This capability leverages the installed base of software related to statistical learning and the accumulated knowledge of how to develop such software. This installed base covers scientific programming languages, scientific routines written in general purpose programming languages (e.g., C), scientific computing extensions to general-purpose programming languages (e.g., scikit-learn for Python), commercial statistical environments (e.g., SAS/STAT), and open source statistical environments (e.g., R). When used to incorporate the capabilities of such a software component, the modeling task builder 730 may require a specification of the software component's inputs and outputs, and/or a characterization of what types of operations the software component can perform. In some embodiments, the modeling task builder 730 generates this metadata by inspecting a software component's source code signature, retrieving the software components' interface definition from a repository, probing the software component with a sequence of requests, or performing some other form of automated evaluation. In some embodiments, the developer manually supplies some or all of this metadata.

In some embodiments, the modeling task builder 730 uses this metadata to create a “wrapper” that allows it to execute the incorporated software. The modeling task builder 730 may implement such wrappers utilizing any mechanism for integrating software components, including, without limitation, compiling a component's source code into an internal executable, linking a component's object code into an internal executable, accessing a component through an emulator of the computing environment expected by the component's standalone executable, accessing a component's functions running as part of a software service on a local machine, accessing a components functions running as part of a software service on a remote machine, accessing a component's function through an intermediary software service running on a local or remote machine, etc. No matter which incorporation mechanism the modeling task builder 730 uses, after the wrapper has been generated, modeling tool 700 may make software calls to the component as it would any other routine.

In some embodiments, developers may use the modeling task builder 730 to assemble leaf-level modeling tasks recursively into higher-level tasks. As indicated previously, there are many different ways to implement the user interface for specifying the arrangement of the task hierarchy. But from a logical perspective, a task that is not at the leaf-level may include a directed graph of sub-tasks. At each of the top and intermediate levels of this hierarchy, there may be one starting sub-task whose input is from the parent task in the hierarchy (or the parent modeling technique at the top level of the hierarchy). There may also be one ending sub-task whose output is to the parent task in the hierarchy (or the parent modeling technique at the top level of the hierarchy). Every other sub-task at a given level may receive inputs from one or more previous sub-tasks and sends outputs to one or more subsequent sub-tasks.

Combined with the ability to incorporate arbitrary code in leaf-level tasks, propagating data according to the directed graph facilitates implementation of arbitrary control flows within an intermediate-level task. In some embodiments, modeling tool 700 may provide additional built-in operations. For example, while it would be straightforward to implement any particular conditional logic as a leaf-level task coded in an external programming language, the modeling task builder 730 may provide a built-in node or arc that performs conditional evaluations in a general fashion, directing some or all of the data from a node to different subsequent nodes based on the results of these evaluations. Similar alternatives exist for filtering the output from one node according to a rule or expression before propagating it as input to subsequent nodes, transforming the output from one node before propagating it as input to subsequent nodes, partitioning the output from one node according to a rule or expression before propagating each partition to a respective subsequent node, combining the output of multiple previous nodes according to a rule or formula before accepting it as input, iteratively applying a sub-graph of nodes' operations using one or more loop variables, etc.

In some embodiments, developers may use the modeling technique builder 720 to assemble tasks from the modeling task library 732 into modeling techniques. At least some of the modeling tasks in modeling task library 732 may correspond to the pre-processing steps, model-fitting steps, and/or post-processing steps of one or more modeling techniques. The development of tasks and techniques may follow a linear pattern, in which techniques are assembled after the task library 732 is populated, or a more dynamic, circular pattern, in which tasks and techniques are assembled concurrently. A developer may be inspired to combine existing tasks into a new technique, realize that this technique requires new tasks, and iteratively refine until the new technique is complete. Alternatively, a developer may start with the conception of a new technique, perhaps from an academic publication, begin building it from new tasks, but pull existing tasks from the modeling task library 732 when they provide suitable functionality. In all cases, the results from applying a modeling technique to reference datasets or in field tests will allow the developer or analyst to evaluate the performance of the technique. This evaluation may, in turn, result in changes anywhere in the hierarchy from leaf-level modeling task to modeling technique. By providing common modeling task and modeling technique libraries (732, 736) as well as high productivity builder interfaces (710, 720, and 730), modeling tool 700 may enable developers to make changes rapidly and accurately, as well as propagate such enhancements to other developers and users with access to the libraries (732, 734).

A modeling technique may provide a focal point for developers and analysts to conceptualize an entire predictive modeling procedure, with all the steps expected based on the best practices in the field. In some embodiments, modeling techniques encapsulate best practices from statistical learning disciplines. Moreover, the modeling tool 700 can provide guidance in the development of high-quality techniques by, for example, providing a checklist of steps for the developer to consider and comparing the task graphs for new techniques to those of existing techniques to, for example, detect missing tasks, detect additional steps, and/or detect anomalous flows among steps.

In some embodiments, exploration engine 610 is used to build a predictive model for a dataset 740 using the techniques in the modeling technique library 630. The exploration engine 610 may prioritize the evaluation of the modeling techniques in modeling technique library 630 based on a prioritization scheme encoded by a modeling methodology selected from the modeling methodology library 712. Examples of suitable prioritization schemes for exploration of the modeling space are described in the next section. In the example of FIG. 7, results of the exploration of the modeling space may be used to update the metadata associated with modeling tasks and techniques.

In some embodiments, unique identifiers (IDs) may be assigned to the modeling elements (e.g., techniques, tasks, and sub-tasks). The ID of a modeling element may be stored as metadata associated with the modeling element's template. In some embodiments, these modeling element IDs may be used to efficiently execute modeling techniques that share one or more modeling tasks or sub-tasks. Methods of efficiently executing modeling techniques are described in further detail below.

In the example of FIG. 7, the modeling results produced by exploration engine 610 are fed back to the modeling task builder 730, the modeling technique builder 720, and the modeling methodology builder 734. The modeling builders may be adapted automatically (e.g., using a statistical learning algorithm) or manually (e.g., by a user) based on the modeling results. For example, modeling methodology builder 734 may be adapted based on patterns observed in the modeling results and/or based on a data analyst's experience. Similarly, results from executing specific modeling techniques may inform automatic or manual adjustment of default tuning parameter values for those techniques or tasks within them. In some embodiments, the adaptation of the modeling builders may be semi-automated. For example, predictive modeling system 600 may flag potential improvements to methodologies, techniques, and/or tasks, and a user may decide whether to implement those potential improvements.

The technical solution can include or utilize a modeling space exploration engine. FIG. 8 is a flowchart of a method 800 for selecting a predictive model for a prediction problem, in accordance with some embodiments. In some embodiments, method 800 may correspond to a modeling methodology in the modeling methodology library 712.

At step 810 of method 800, the suitability of a plurality of predictive modeling procedures (e.g., predictive modeling techniques) for a prediction problem are determined. A predictive modeling procedure's suitability for a prediction problem may be determined based on characteristics of the prediction problem, based on attributes of the modeling procedures, and/or based on other suitable information.

The “suitability” of a predictive modeling procedure for a prediction problem may include data indicative of the expected performance on the prediction problem of predictive models generated using the predictive modeling procedure. In some embodiments, a predictive model's expected performance on a prediction problem includes one or more expected scores (e.g., expected values of one or more objective functions) and/or one or more expected ranks (e.g., relative to other predictive models generated using other predictive modeling techniques).

Alternatively or in addition, the “suitability” of a predictive modeling procedure for a prediction problem may include data indicative of the extent to which the modeling procedure is expected to generate predictive models that provide adequate performance for a prediction problem. In some embodiments, a predictive modeling procedure's “suitability” data includes a classification of the modeling procedure's suitability. The classification scheme may have two classes (e.g., “suitable” or “not suitable”) or more than two classes (e.g., “highly suitable”, “moderately suitable”, “moderately unsuitable”, “highly unsuitable”).

In some embodiments, exploration engine 610 determines the suitability of a predictive modeling procedure for a prediction problem based, at least in part, on one or more characteristics of the prediction problem, including (but not limited to) characteristics described herein. As just one example, the suitability of a predictive modeling procedure for a prediction problem may be determined based on characteristics of the dataset corresponding to the prediction problem, characteristics of the variables in the dataset corresponding to the prediction problem, relationships between the variables in the dataset, and/or the subject matter of the prediction problem. Exploration engine 610 may include tools (e.g., statistical analysis tools) for analyzing datasets associated with prediction problems to determine the characteristics of the prediction problems, the datasets, the dataset variables, etc.

In some embodiments, exploration engine 610 determines the suitability of a predictive modeling procedure for a prediction problem based, at least in part, on one or more attributes of the predictive modeling procedure, including (but not limited to) the attributes of predictive modeling procedures described herein. As just one example, the suitability of a predictive modeling procedure for a prediction problem may be determined based on the data processing techniques performed by the predictive modeling procedure and/or the data processing constraints imposed by the predictive modeling procedure.

In some embodiments, determining the suitability of the predictive modeling procedures for the prediction problem comprises eliminating at least one predictive modeling procedure from consideration for the prediction problem. The decision to eliminate a predictive modeling procedure from consideration may be referred to herein as “pruning” the eliminated modeling procedure and/or “pruning the search space”. In some embodiments, the user can override the exploration engine's decision to prune a modeling procedure, such that the previously pruned modeling procedure remains eligible for further execution and/or evaluation during the exploration of the search space.

A predictive modeling procedure may be eliminated from consideration based on the results of applying one or more deductive rules to the attributes of the predictive modeling procedure and the characteristics of the prediction problem. The deductive rules may include, without limitation, the following: (1) if the prediction problem includes a categorical target variable, select only classification techniques for execution; (2) if numeric features of the dataset span vastly different magnitude ranges, select or prioritize techniques that provide normalization; (3) if a dataset has text features, select or prioritize techniques that provide text mining; (4) if the dataset has more features than observations, eliminate all techniques that require the number of observations to be greater than or equal to the number of features; (5) if the width of the dataset exceeds a threshold width, select or prioritize techniques that provide dimension reduction; (6) if the dataset is large and sparse (e.g., the size of the dataset exceeds a threshold size and the sparseness of the dataset exceeds a threshold sparseness), select or prioritize techniques that execute efficiently on sparse data structures; and/or any rule for selecting, prioritizing, or eliminating a modeling technique wherein the rule can be expressed in the form of an if-then statement. In some embodiments, deductive rules are chained so that the execution of several rules in sequence produces a conclusion. In some embodiments, the deductive rules may be updated, refined, or improved based on historical performance.

In some embodiments, exploration engine 610 determines the suitability of a predictive modeling procedure for a prediction problem based on the performance (expected or actual) of similar predictive modeling procedures on similar prediction problems. (As a special case, exploration engine 610 may determine the suitability of a predictive modeling procedure for a prediction problem based on the performance (expected or actual) of the same predictive modeling procedure on similar prediction problems.)

As described above, the library of modeling techniques 630 may include tools for assessing the similarities between predictive modeling techniques, and the library of prediction problems may include tools for assessing the similarities between prediction problems. Exploration engine 610 may use these tools to identify predictive modeling procedures and prediction problems similar to the predictive modeling procedure and prediction problem at issue. For purposes of determining the suitability of a predictive modeling procedure for a prediction problem, exploration engine 610 may select the M modeling procedures most similar to the modeling procedure at issue, select all modeling procedures exceeding a threshold similarity value with respect to the modeling procedure at issue, etc. Likewise, for purposes of determining the suitability of a predictive modeling procedure for a prediction problem, exploration engine 610 may select the N prediction problems most similar to the prediction problem at issue, select all prediction problems exceeding a threshold similarity value with respect to the prediction problem at issue, etc.

Given a set of predictive modeling procedures and a set of prediction problems similar to the modeling procedure and prediction problem at issue, exploration engine may combine the performances of the similar modeling procedures on the similar prediction problems to determine the expected suitability of the modeling procedure at issue for the prediction problem at issue. As described above, the templates of modeling procedures may include information relevant to estimating how well the corresponding modeling procedure will perform for a given dataset. Exploration engine 610 may use the model performance metadata to determine the performance values (expected or actual) of the similar modeling procedures on the similar prediction problems. These performance values can then be combined to generate an estimate of the suitability of the modeling procedure at issue for the prediction problem at issue. For example, exploration engine 610 may calculate the suitability of the modeling procedure at issue as a weighted sum of the performance values of the similar modeling procedures on the similar prediction problems.

In some embodiments, exploration engine 610 determines the suitability of a predictive modeling procedure for a prediction problem based, at least in part, on the output of a “meta” machine-learning model, which may be trained to determine the suitability of a modeling procedure for a prediction problem based on the results of various modeling procedures (e.g., modeling procedures similar to the modeling procedure at issue) for other prediction problems (e.g., prediction problems similar to the prediction problem at issue). The machine-learning model for estimating the suitability of a predictive modeling procedure for a prediction problem may be referred to as a “meta” machine-learning model because it applies machine learning recursively to predict which techniques are most likely to succeed for the prediction problem at issue. Exploration engine 610 may therefore produce meta-predictions of the suitability of a modeling technique for a prediction problem by using a meta-machine-learning algorithm trained on the results from solving other prediction problems.

In some embodiments, exploration engine 610 may determine the suitability of a predictive modeling procedure for a prediction problem based, at least in part, on user input (e.g., user input representing the intuition or experience of data analysts regarding the predictive modeling procedure's suitability).

Returning to FIG. 8, at step 820 of method 800, at least a subset of the predictive modeling procedures may be selected based on the suitability of the modeling procedures for the prediction problem. In embodiments where the modeling procedures have been assigned to suitability categories (e.g., “suitable” or “not suitable”; “highly suitable”, “moderately suitable”, “moderately unsuitable”, or “highly unsuitable”; etc.), selecting a subset of the modeling procedures may comprise selecting the modeling procedures assigned to one or more suitability categories (e.g., all modeling procedures assigned to the “suitable category”; all modeling procedures not assigned to the “highly unsuitable” category; etc.).

In embodiments where the modeling procedures have been assigned suitability values, exploration engine 610 may select a subset of the modeling procedures based on the suitability values. In some embodiments, exploration engine 610 selects the modeling procedures with suitability scores above a threshold suitability score. The threshold suitability score may be provided by a user or determined by exploration engine 610. In some embodiments, exploration engine 610 may adjust the threshold suitability score to increase or decrease the number of modeling procedures selected for execution, depending on the amount of processing resources available for execution of the modeling procedures.

In some embodiments, exploration engine 610 selects the modeling procedures with suitability scores within a specified range of the highest suitability score assigned to any of the modeling procedures for the prediction problem at issue. The range may be absolute (e.g., scores within S points of the highest score) or relative (e.g., scores within P % of the highest score). The range may be provided by a user or determined by exploration engine 610. In some embodiments, exploration engine 610 may adjust the range to increase or decrease the number of modeling procedures selected for execution, depending on the amount of processing resources available for execution of the modeling procedures.

In some embodiments, exploration engine 610 selects a fraction of the modeling procedures having the highest suitability scores for the prediction problem at issue. Equivalently, the exploration engine 610 may select the fraction of the modeling procedures having the highest suitability ranks (e.g., in cases where the suitability scores for the modeling procedures are not available, but the ordering (ranking) of the modeling procedures' suitability is available). The fraction may be provided by a user or determined by exploration engine 610. In some embodiments, exploration engine 610 may adjust the fraction to increase or decrease the number of modeling procedures selected for execution, depending on the amount of processing resources available for execution of the modeling procedures.

In some embodiments, a user may select one or more modeling procedures to be executed. The user-selected procedures may be executed in addition to or in lieu of one or more modeling procedures selected by exploration engine 610. Allowing the users to select modeling procedures for execution may improve the performance of predictive modeling system 600, particularly in scenarios where a data analyst's intuition and experience indicate that the modeling system 600 has not accurately estimated a modeling procedure's suitability for a prediction problem.

In some embodiments, exploration engine 610 may control the granularity of the search space evaluation by selecting a modeling procedure P0 that is representative of (e.g., similar to) one or more other modeling procedures P1 . . . PN, rather than selecting modeling procedures P0 . . . PN, even if modeling procedures P0 . . . PN are all determined to be suitable for the prediction problem at issue. In addition, exploration engine 610 may treat the results of executing the selected modeling procedure P0 as being representative of the results of executing the modeling procedures P1 . . . PN. This coarse-grained approach to evaluating the search space may conserve processing resources, particularly if applied during the earlier stages of the evaluation of the search space. If exploration engine 610 later determines that modeling procedure P0 is among the most suitable modeling procedures for the prediction problem, a fine-grained evaluation of the relevant portion of the search space can then be performed by executing and evaluating the similar modeling procedures P1 . . . PN.

Returning to FIG. 8, at step 830 of method 800, a resource allocation schedule may be generated. The resource allocation schedule may allocate processing resources for the execution of the selected modeling procedures. In some embodiments, the resource allocation schedule allocates the processing resources to the modeling procedures based on the determined suitability of the modeling procedures for the prediction problem at issue. In some embodiments, exploration engine 610 transmits the resource allocation schedule to one or more processing nodes with instructions for executing the selected modeling procedures according to the resource allocation schedule.

The allocated processing resources may include temporal resources (e.g., execution cycles of one or more processing nodes, execution time on one or more processing nodes, etc.), physical resources (e.g., a number of processing nodes, an amount of machine-readable storage (e.g., memory and/or secondary storage), etc.), and/or other allocable processing resources. In some embodiments, the allocated processing resources may be processing resources of a distributed computing system and/or a cloud-based computing system. In some embodiments, costs may be incurred when processing resources are allocated and/or used (e.g., fees may be collected by an operator of a data center in exchange for using the data center's resources).

As indicated above, the resource allocation schedule may allocate processing resources to modeling procedures based on the suitability of the modeling procedures for the prediction problem at issue. For example, the resource allocation schedule may allocate more processing resources to modeling procedures with higher predicted suitability for the prediction problem, and allocate fewer processing resources to modeling procedures with lower predicted suitability for the prediction problem, so that the more promising modeling procedures benefit from a greater share of the limited processing resources. As another example, the resource allocation schedule may allocate processing resources sufficient for processing larger datasets to modeling procedures with higher predicted suitability, and allocate processing resources sufficient for processing smaller datasets to modeling procedures with lower predicted suitability.

As another example, the resource allocation schedule may schedule execution of the modeling procedures with higher predicted suitability prior to execution of the modeling procedures with lower predicted suitability, which may also have the effect of allocating more processing resources to the more promising modeling procedures. In some embodiments, the results of executing the modeling procedures may be presented to the user via user interface 620 as the results become available. In such embodiments, scheduling the modeling procedures with higher predicted suitability to execute before the modeling procedures with lower predicted suitability may provide the user with additional information about the evaluation of the search space at an earlier phase of the evaluation, thereby facilitating rapid user-driven adjustments to the search plan. For example, based on the preliminary results, the user may determine that one or more modeling procedures that were expected to perform very well are actually performing very poorly. The user may investigate the cause of the poor performance and determine, for example, that the poor performance is caused by an error in the preparation of the dataset. The user can then fix the error and restart execution of the modeling procedures that were affected by the error.

In some embodiments, the resource allocation schedule may allocate processing resources to modeling procedures based, at least in part, on the resource utilization characteristics and/or parallelism characteristics of the modeling procedures. As described above, the template corresponding to a modeling procedure may include metadata relevant to estimating how efficiently the modeling procedure will execute on a distributed computing infrastructure. In some embodiments, this metadata includes an indication of the modeling procedure's resource utilization characteristics (e.g., the processing resources needed to train and/or test the modeling procedure on a dataset of a given size). In some embodiments, this metadata includes an indication of the modeling procedure's parallelism characteristics (e.g., the extent to which the modeling procedure can be executed in parallel on multiple processing nodes). Using the resource utilization characteristics and/or parallelism characteristics of the modeling procedures to determine the resource allocation schedule may facilitate efficient allocation of processing resources to the modeling procedures.

In some embodiments, the resource allocation schedule may allocate a specified amount of processing resources for the execution of the modeling procedures. The allocable amount of processing resources may be specified in a processing resource budget, which may be provided by a user or obtained from another suitable source. The processing resource budget may impose limits on the processing resources to be used for executing the modeling procedures (e.g., the amount of time to be used, the number of processing nodes to be used, the cost incurred for using a data center or cloud-based processing resources, etc.). In some embodiments, the processing resource budget may impose limits on the total processing resources to be used for the process of generating a predictive model for a specified prediction problem.

Returning to FIG. 8, at step 840 of method 800, the results of executing the selected modeling procedures in accordance with the resource allocation schedule may be received. These results may include one or more predictive models generated by the executed modeling procedures. In some embodiments, the predictive models received at step 840 are fitted to dataset(s) associated with the prediction problem, because the execution of the modeling procedures may include fitting of the predictive models to one or more datasets associated with the prediction problem. Fitting the predictive models to the prediction problem's dataset(s) may include tuning one or more hyper-parameters of the predictive modeling procedure that generates the predictive model, tuning one or more parameters of the generated predictive model, and/or other suitable model-fitting steps.

In some embodiments, the results received at step 840 include evaluations (e.g., scores) of the models' performances on the prediction problem. These evaluations may be obtained by testing the predictive models on test dataset(s) associated with the prediction problem. In some embodiments, testing a predictive model includes cross-validating the model using different folds of training datasets associated with the prediction problem. In some embodiments, the execution of the modeling procedures includes the testing of the generated models. In some embodiments, the testing of the generated models is performed separately from the execution of the modeling procedures.

The models may be tested in accordance with suitable testing techniques and scored according to a suitable scoring metric (e.g., an objective function). Different scoring metrics may place different weights on different aspects of a predictive model's performance, including, without limitation, the model's accuracy (e.g., the rate at which the model correctly predicts the outcome of the prediction problem), false positive rate (e.g., the rate at which the model incorrectly predicts a “positive” outcome), false negative rate (e.g., the rate at which the model incorrectly predicts a “negative” outcome), positive prediction value, negative prediction value, sensitivity, specificity, etc. The user may select a standard scoring metric (e.g., goodness-of-fit, R-square, etc.) from a set of options presented via user interface 620, or specific a custom scoring metric (e.g., a custom objective function) via user interface 620. Exploration engine 610 may use the user-selected or user-specified scoring metric to score the performance of the predictive models.

Returning to FIG. 8, at step 850 of method 800, a predictive model may be selected for the prediction problem based on the evaluations (e.g., scores) of the generated predictive models. Space search engine 610 may use any suitable criteria to select the predictive model for the prediction problem. In some embodiments, space search engine 610 may select the model with the highest score, or any model having a score that exceeds a threshold score, or any model having a score within a specified range of the highest score. In some embodiments, the predictive models' scores may be just one factor considered by space exploration engine 610 in selecting a predictive model for the prediction problem. Other factors considered by space exploration engine may include, without limitation, the predictive model's complexity, the computational demands of the predictive model, etc.

In some embodiments, selecting the predictive model for the prediction problem may comprise iteratively selecting a subset of the predictive models and training the selected predictive models on larger or different portions of the dataset. This iterative process may continue until a predictive model is selected for the prediction problem or until the processing resources budgeted for generating the predictive model are exhausted.

Selecting a subset of predictive models may comprise selecting a fraction of the predictive models with the highest scores, selecting all models having scores that exceed a threshold score, selecting all models having scores within a specified range of the score of the highest-scoring model, or selecting any other suitable group of models. In some embodiments, selecting the subset of predictive models may be analogous to selecting a subset of predictive modeling procedures, as described above with reference to step 820 of method 800. Accordingly, the details of selecting a subset of predictive models are not belabored here.

Training the selected predictive models may comprise generating a resource allocation schedule that allocates processing resources of the processing nodes for the training of the selected models. The allocation of processing resources may be determined based, at least in part, on the suitability of the modeling techniques used to generate the selected models, and/or on the selected models' scores for other samples of the dataset. Training the selected predictive models may further comprise transmitting instructions to processing nodes to fit the selected predictive models to a specified portion of the dataset, and receiving results of the training process, including fitted models and/or scores of the fitted models. In some embodiments, training the selected predictive models may be analogous to executing the selected predictive modeling procedures, as described above with reference to steps 820-840 of method 800. Accordingly, the details of training the selected predictive models are not belabored here.

In some embodiments, steps 830 and 840 may be performed iteratively until a predictive model is selected for the prediction problem or until the processing resources budgeted for generating the predictive model are exhausted. At the end of each iteration, the suitability of the predictive modeling procedures for the prediction problem may be re-determined based, at least in part, on the results of executing the modeling procedures, and a new set of predictive modeling procedures may be selected for execution during the next iteration.

In some embodiments, the number of modeling procedures executed in an iteration of steps 830 and 840 may tend to decrease as the number of iterations increases, and the amount of data used for training and/or testing the generated models may tend to increase as the number of iterations increases. Thus, the earlier iterations may “cast a wide net” by executing a relatively large number of modeling procedures on relatively small datasets, and the later iterations may perform more rigorous testing of the most promising modeling procedures identified during the earlier iterations. Alternatively or in addition, the earlier iterations may implement a more coarse-grained evaluation of the search space, and the later iterations may implement more fine-grained evaluations of the portions of the search space determined to be most promising.

In some embodiments, method 800 includes one or more steps not illustrated in FIG. 8. Additional steps of method 800 may include, without limitation, processing a dataset associated with the prediction problem, blending two or more predictive models to form a blended predictive model, and/or tuning the predictive model selected for the prediction problem. Some embodiments of these steps are described in further detail below.

Method 800 may include a step in which the dataset associated with a prediction problem is processed. In some embodiments, processing a prediction problem's dataset includes characterizing the dataset. Characterizing the dataset may include identifying potential problems with the dataset, including but not limited to identifying data leaks (e.g., scenarios in which the dataset includes a feature that is strongly correlated with the target, but the value of the feature would not be available as input to the predictive model under the conditions imposed by the prediction problem), detecting missing observations, detecting missing variable values, identifying outlying variable values, and/or identifying variables that are likely to have significant predictive value (“predictive variables”).

In some embodiments, processing a prediction problem's dataset includes applying feature engineering to the dataset. Applying feature engineering to the dataset may include combining two or more features and replacing the constituent features with the combined feature, extracting different aspects of date/time variables (e.g., temporal and seasonal information) into separate variables, normalizing variable values, infilling missing variable values, etc.

Method 800 may include a step in which two or more predictive models are blended to form a blended predictive model. The blending step may be performed iteratively in connection with executing the predictive modeling techniques and evaluating the generated predictive models. In some embodiments, the blending step may be performed in only some of the execution/evaluation iterations (e.g., in the later iterations, when multiple promising predictive models have been generated).

Two or more models may be blended by combining the outputs of the constituent models. In some embodiments, the blended model may comprise a weighted, linear combination of the outputs of the constituent models. A blended predictive model may perform better than the constituent predictive models, particularly in cases where different constituent models are complementary. For example, a blended model may be expected to perform well when the constituent models tend to perform well on different portions of the prediction problem's dataset, when blends of the models have performed well on other (e.g., similar) prediction problems, when the modeling techniques used to generate the models are dissimilar (e.g., one model is a linear model and the other model is a tree model), etc. In some embodiments, the constituent models to be blended together are identified by a user (e.g., based on the user's intuition and experience).

Method 800 may include a step in which the predictive model selected for the prediction problem is tuned. In some cases, deployment engine 640 provides the source code that implements the predictive model to the user, thereby enabling the user to tune the predictive model. However, disclosing a predictive model's source code may be undesirable in some cases (e.g., in cases where the predictive modeling technique or predictive model contains proprietary capabilities or information). To permit a user to tune a predictive model without exposing the model's source code, deployment engine 640 may construct human-readable rules for tuning the model's parameters based on a representation (e.g., a mathematical representation) of the predictive model, and provide the human-readable rules to the user. The user can then use the human-readable rules to tune the model's parameters without accessing the model's source code. Thus, predictive modeling system 600 may support evaluation and tuning of proprietary predictive modeling techniques without exposing the source code for the proprietary modeling techniques to end users.

In some embodiments, the machine-executable templates corresponding to predictive modeling procedures may include efficiency-enhancing features to reduce redundant computation. These efficiency-enhancing features can be particularly valuable in cases where relatively small amounts of processing resources are budgeted for exploring the search space and generating the predictive model. As described above, the machine-executable templates may store unique IDs for the corresponding modeling elements (e.g., techniques, tasks, or sub-tasks). In addition, predictive modeling system 600 may assign unique IDs to dataset samples S. In some embodiments, when a machine-executable template T is executed on a dataset sample S, the template stores its modeling element ID, the dataset/sample ID, and the results of executing the template on the data sample in a storage structure (e.g., a table, a cache, a hash, etc.) accessible to the other templates. When a template T is invoked on a dataset sample S, the template checks the storage structure to determine whether the results of executing that template on that dataset sample are already stored. If so, rather than reprocessing the dataset sample to obtain the same results, the template simply retrieves the corresponding results from the storage structure, returns those results, and terminates. The storage structure may persist within individual iterations of the loop in which modeling procedures are executed, across multiple iterations of the procedure-execution loop, or across multiple search space explorations. The computational savings achieved through this efficiency-enhancing feature can be appreciable, since many tasks and sub-tasks are shared by different modeling techniques, and method 800 often involves executing different modeling techniques on the same datasets.

FIG. 9 shows a flowchart of a method 900 for selecting a predictive model for a prediction problem, in accordance with some embodiments. Method 800 may be embodied by the example of method 900.

In the example of FIG. 9, space exploration engine 610 uses the modeling methodology library 712, the modeling technique library 630, and the modeling task library 732 to search the space of available modeling techniques for a solution to a predictive modeling problem. Initially, the user may select a modeling methodology from library 712, or space exploration engine 610 may automatically select a default modeling methodology. The available modeling methodologies may include, without limitation, selection of modeling techniques based on application of deductive rules, selection of modeling techniques based on the performance of similar modeling techniques on similar prediction problems, selection of modeling techniques based on the output of a meta machine-learning model, any combination of the foregoing modeling techniques, or other suitable modeling techniques.

At step 902 of method 900, the exploration engine 610 prompts the user to select the dataset for the predictive modeling problem to be solved. The user can chose from previously loaded datasets or create a new dataset, either from a file or instructions for retrieving data from other information systems. In the case of files, the exploration engine 610 may support one or more formats including, without limitation, comma separated values, tab-delimited, eXtensible Markup Language (XML), JavaScript Object Notation, native database files, etc. In the case of instructions, the user may specify the types of information systems, their network addresses, access credentials, references to the subsets of data within each system, and the rules for mapping the target data schemas into the desired dataset schema. Such information systems may include, without limitation, databases, data warehouses, data integration services, distributed applications, Web services, etc.

At step 504 of method 900, exploration engine 610 loads the data (e.g., by reading the specified file or accessing the specified information systems). Internally, the exploration engine 610 may construct a two-dimensional matrix with the features on one axis and the observations on the other. Conceptually, each column of the matrix may correspond to a variable, and each row of the matrix may correspond to an observation. The exploration engine 610 may attach relevant metadata to the variables, including metadata obtained from the original source (e.g., explicitly specified data types) and/or metadata generated during the loading process (e.g., the variable's apparent data types; whether the variables appear to be numerical, ordinal, cardinal, or interpreted types; etc.).

At step 906 of method 900, exploration engine 610 prompts the user to identify which of the variables are targets and/or which are features. In some embodiments, exploration engine 610 also prompts the user to identify the metric of model performance to be used for scoring the models (e.g., the metric of model performance to be optimized, in the sense of statistical optimization techniques, by the statistical learning algorithm implemented by exploration engine 610).

At step 908 of method 900, exploration engine 610 evaluates the dataset. This evaluation may include calculating the characteristics of the dataset. In some embodiments, this evaluation includes performing an analysis of the dataset, which may help the user better understand the prediction problem. Such an analysis may include applying one or more algorithms to identify problematic variables (e.g., those with outliers or inliers), determining variable importance, determining variable effects, and identifying effect hotspots.

The analysis of the dataset may be performed using any suitable techniques. Variable importance, which measures the degree of significance each feature has in predicting the target, may be analyzed using “gradient boosted trees”, Breiman and Cutler's “Random Forest”, “alternating conditional expectations”, and/or other suitable techniques. Variable effects, which measure the directions and sizes of the effects features have on a target, may be analyzed using “regularized regression”, “logistic regression”, and/or other suitable techniques. Effect hotspots, which identify the ranges over which features provide the most information in predicting the target, may be analyzed using the “RuleFit” algorithm and/or other suitable techniques.

In some embodiments, in addition to assessing the importance of features contained in the original dataset, the evaluation performed at step 908 of method 900 includes feature generation. Feature generation techniques may include generating additional features by interpreting the logical type of the dataset's variable and applying various transformations to the variable. Examples of transformations include, without limitation, polynomial and logarithmic transformations for numeric features. For interpreted variables (e.g., date, time, currency, measurement units, percentages, and location coordinates), examples of transformations include, without limitation, parsing a date string into a continuous time variable, day of week, month, and season to test each aspect of the date for predictive power.

The systematic transformation of numeric and/or interpreted variables, followed by their systematic testing with potential predictive modeling techniques may enable predictive modeling system 600 to search more of the potential model space and achieve more precise predictions. For example, in the case of “date/time”, separating temporal and seasonal information into separate features can be very beneficial because these separate features often exhibit very different relationships with the target variable.

Creating derived features by interpreting and transforming the original features can increase the dimensionality of the original dataset. The predictive modeling system 600 may apply dimension reduction techniques, which may counter the increase in the dataset's dimensionality. However, some modeling techniques are more sensitive to dimensionality than others. Also, different dimension reduction techniques tend to work better with some modeling techniques than others. In some embodiments, predictive modeling system 600 maintains metadata describing these interactions. The system 600 may systematically evaluate various combinations of dimension reduction techniques and modeling techniques, prioritizing the combinations that the metadata indicate are most likely to succeed. The system 600 may further update this metadata based on the empirical performance of the combinations over time and incorporate new dimension reduction techniques as they are discovered.

At step 910 of method 900, predictive modeling system 600 presents the results of the dataset evaluation (e.g., the results of the dataset analysis, the characteristics of the dataset, and/or the results of the dataset transformations) to the user. In some embodiments, the results of the dataset evaluation are presented via user interface 620 (e.g., using graphs and/or tables).

At step 912 of method 900, the user may refine the dataset (e.g., based on the results of the dataset evaluation). Such refinement may include selecting methods for handling missing values or outliers for one or more features, changing an interpreted variable's type, altering the transformations under consideration, eliminating features from consideration, directly editing particular values, transforming features using a function, combining the values of features using a formula, adding entirely new features to the dataset, etc.

Steps 902-912 of method 900 may represent one embodiment of the step of processing a prediction problem's dataset, as described above in connection with some embodiments of method 800.

At step 914 of method 900, the exploration engine 610 may load the available modeling techniques from the modeling technique library 630. The determination of which modeling techniques are available may depend on the selected modeling methodology. In some embodiments, the loading of the modeling techniques may occur in parallel with one or more of steps 902-912 of method 900.

At step 916 of method 900, the user instructs the exploration engine 610 to begin the search for modeling solutions in either manual mode or automatic mode. In automatic mode, the exploration engine 610 partitions the dataset (step 918) using a default sampling algorithm and prioritizes the modeling techniques (step 920) using a default prioritization algorithm. Prioritizing the modeling techniques may include determining the suitability of the modeling techniques for the prediction problem, and selecting at least a subset of the modeling techniques for execution based on their determined suitability.

In manual mode, the exploration engine 610 suggests data partitions (step 922) and suggests a prioritization of the modeling techniques (step 924). The user may accept the suggested data partition or specify custom partitions (step 926). Likewise, the user may accept the suggested prioritization of modeling techniques or specify a custom prioritization of the modeling techniques (step 928). In some embodiments, the user can modify one or more modeling techniques (e.g., using the modeling technique builder 720 and/or the modeling task builder 730) (step 930) before the exploration engine 610 begins executing the modeling techniques.

To facilitate cross-validation, predictive modeling system 600 may partition the dataset (or suggest a partitioning of the dataset) into K “folds”. Cross-validation comprises fitting a predictive model to the partitioned dataset K times, such that during each fitting, a different fold serves as the test set and the remaining folds serve as the training set. Cross-validation can generate useful information about how the accuracy of a predictive model varies with different training data. In steps 918 and 922, predictive modeling system may partition the dataset into K folds, where the number of folds K is a default parameter. In step 926, the user may change the number of folds K or cancel the use of cross-validation altogether.

To facilitate rigorous testing of the predictive models, predictive modeling system 600 may partition the dataset (or suggest a partitioning of the dataset) into a training set and a “holdout” test set. In some embodiments, the training set is further partitioned into K folds for cross-validation. The training set may then be used to train and evaluate the predictive models, but the holdout test set may be reserved strictly for testing the predictive models. In some embodiments, predictive modeling system 600 can strongly enforce the use of the holdout test set for testing (and not for training) by making the holdout test set inaccessible until a user with the designated authority and/or credentials releases it. In steps 918 and 922, predictive modeling system 600 may partition the dataset such that a default percentage of the dataset is reserved for the holdout set. In step 926, the user may change the percentage of the dataset reserved for the holdout set, or cancel the use of a holdout set altogether.

In some embodiments, predictive modeling system 600 partitions the dataset to facilitate efficient use of computing resources during the evaluation of the modeling search space. For example, predictive modeling system 600 may partition the cross-validation folds of the dataset into smaller samples. Reducing the size of the data samples to which the predictive models are fitted may reduce the amount of computing resources needed to evaluate the relative performance of different modeling techniques. In some embodiments, the smaller samples may be generated by taking random samples of a fold's data. Likewise, reducing the size of the data samples to which the predictive models are fitted may reduce the amount of computing resources needed to tune the parameters of a predictive model or the hyper-parameters of a modeling technique. Hyper-parameters include variable settings for a modeling technique that can affect the speed, efficiency, and/or accuracy of model fitting process. Examples of hyper-parameters include, without limitation, the penalty parameters of an elastic-net model, the number of trees in a gradient boosted trees model, the number of neighbors in a nearest neighbors model, etc.

In steps 932-958 of method 900, the selected modeling techniques may be executed using the partitioned data to evaluate the search space. These steps are described in further detail below. For convenience, some aspects of the evaluation of the search space relating to data partitioning are described in the following paragraphs.

Tuning hyper-parameters using sample data that includes the test set of a cross-validation fold can lead to model over-fitting, thereby making comparisons of different models' performance unreliable. Using a “specified approach” can help avoid this problem, and can provide several other advantages. Some embodiments of exploration engine 610 therefore implement “nested cross-validation”, a technique whereby two loops of k-fold cross validation are applied. The outer loop provides a test set for both comparing a given model to other models and calibrating each model's predictions on future samples. The inner loop provides both a test set for tuning the hyper-parameters of the given model and a training set for derived features.

Moreover, the cross-validation predictions produced in the inner loop may facilitate blending techniques that combine multiple different models. In some embodiments, the inputs into a blender are predictions from an out-of-sample model. Using predictions from an in-sample model could result in over-fitting if used with some blending algorithms. Without a well-defined process for consistently applying nested cross-validation, even the most experienced users can omit steps or implement them incorrectly. Thus, the application of a double loop of k-fold cross validation may allow predictive modeling system 600 to simultaneously achieve five goals: (1) tuning complex models with many hyper-parameters, (2) developing informative derived features, (3) tuning a blend of two or more models, (4) calibrating the predictions of single and/or blended models, and (5) maintaining a pure untouched test set that allows an accurate comparison of different models.

At step 932 of method 900, the exploration engine 610 generates a resource allocation schedule for the execution of an initial set of the selected modeling techniques. The allocation of resources represented by the resource allocation schedule may be determined based on the prioritization of modeling techniques, the partitioned data samples, and the available computation resources. In some embodiments, exploration engine 610 allocates resources to the selected modeling techniques greedily (e.g., assigning computational resources in turn to the highest-priority modeling technique that has not yet executed).

At step 934 of method 900, the exploration engine 610 initiates execution of the modeling techniques in accordance with the resource allocation schedule. In some embodiments, execution of a set of modeling techniques may comprise training one or more models on a same data sample extracted from the dataset.

At step 936 of method 900, the exploration engine 610 monitors the status of execution of the modeling techniques. When a modeling technique is finished executing, the exploration engine 610 collects the results (step 938), which may include the fitted model and/or metrics of model fit for the corresponding data sample. Such metrics may include any metric that can be extracted from the underlying software components that perform the fitting, including, without limitation, Gini coefficient, r-squared, residual mean squared error, any variations thereof, etc.

At step 940 of method 900, the exploration engine 610 eliminates the worst-performing modeling techniques from consideration (e.g., based on the performance of the models they produced according to model fit metrics). Exploration engine 610 may determine which modeling techniques to eliminate using a suitable technique, including, without limitation, eliminating those that do not produce models that meet a minimum threshold value of a model fit metric, eliminating all modeling techniques except those that have produced models currently in the top fraction of all models produced, or eliminating any modeling techniques that have not produced models that are within a certain range of the top models. In some embodiments, different procedures may be used to eliminate modeling techniques at different stages of the evaluation. In some embodiments, users may be permitted to specify different elimination-techniques for different modeling problems. In some embodiments, users may be permitted to build and use custom elimination techniques. In some embodiments, meta-statistical-learning techniques may be used to choose among elimination-techniques and/or to adjust the parameters of those techniques.

As the exploration engine 610 calculates model performance and eliminates modeling techniques from consideration, predictive modeling system 600 may present the progress of the search space evaluation to the user through the user interface 620 (step 942). In some embodiments, at step 944, exploration engine 610 permits the user to modify the process of evaluating the search space based on the progress of the search space evaluation, the user's expert knowledge, and/or other suitable information. If the user specifies a modification to the search space evaluation process, the space exploration engine 610 reallocates processing resources accordingly (e.g., determines which jobs are affected and either moves them within the scheduling queue or deletes them from the queue). Other jobs continue processing as before.

The user may modify the search space evaluation process in many different ways. For example, the user may reduce the priority of some modeling techniques or eliminate some modeling techniques from consideration altogether even though the performance of the models they produced on the selected metric was good. As another example, the user may increase the priority of some modeling techniques or select some modeling techniques for consideration even though the performance of the models they produced was poor. As another example, the user may prioritize evaluation of specified models or execution of specified modeling techniques against additional data samples. As another example, a user may modify one or more modeling techniques and select the modified techniques for consideration. As another example, a user may change the features used to train the modeling techniques or fit the models (e.g., by adding features, removing features, or selecting different features). Such a change may be beneficial if the results indicate that the feature magnitudes require normalizations or that some of the features are “data leaks”.

In some embodiments, steps 932-944 may be performed iteratively. Modeling techniques that are not eliminated (e.g., by the system at step 940 or by the user at step 944) survive another iteration. Based on the performance of a model generated in the previous iteration (or iterations), the exploration engine 610 adjusts the corresponding modeling technique's priority and allocates processing resources to the modeling technique accordingly. As computational resources become available, the engine uses the available resources to launch model-technique-execution jobs based on the updated priorities.

In some embodiments, at step 932, exploration engine 610 may “blend” multiple models using different mathematical combinations to create new models (e.g., using stepwise selection of models to include in the blender). In some embodiments, predictive modeling system 600 provides a modular framework that allows users to plug in their own automatic blending techniques. In some embodiments, predictive modeling system 600 allows users to manually specify different model blends.

In some embodiments, predictive modeling system 600 may offer one or more advantages in developing blended prediction models. First, blending may work better when a large variety of candidate models are available to blend. Moreover, blending may work better when the differences between candidate models correspond not simply to minor variations in algorithms but rather to major differences in approach, such as those among linear models, tree-based models, support vector machines, and nearest neighbor classification. Predictive modeling system 600 may deliver a substantial head start by automatically producing a wide variety of models and maintaining metadata describing how the candidate models differ. Predictive modeling system 600 may also provide a framework that allows any model to be incorporated into a blended model by, for example, automatically normalizing the scale of variables across the candidate models. This framework may allow users to easily add their own customized or independently generated models to the automatically generated models to further increase variety.

In addition to increasing the variety of candidate models available for blending, the predictive modeling system 600 also provides a number of user interface features and analytic features that may result in superior blending. First, user interface 620 may provide an interactive model comparison, including several different alternative measures of candidate model fit and graphics such as dual lift charts, so that users can easily identify accurate and complementary models to blend. Second, modeling system 600 gives the user the option of choosing specific candidate models and blending techniques or automatically fitting some or all of the blending techniques in the modeling technique library using some or all of the candidate models. The nested cross-validation framework then enforces the condition that the data used to rank each blended model is not used in tuning the blender itself or in tuning its component models' hyper-parameters. This discipline may provide the user a more accurate comparison of alternative blender performance. In some embodiments, modeling system 600 implements a blended model's processing in parallel, such that the computation time for the blended model approaches the computation time of its slowest component model.

Returning to FIG. 9, at step 946 of method 900, the user interface 620 presents the final results to the user. Based on this presentation, the user may refine the dataset (e.g., by returning to step 912), adjust the allocation of resources to executing modeling techniques (e.g., by returning to step 944), modify one or more of the modeling techniques to improve accuracy (e.g., by returning to step 930), alter the dataset (e.g., by returning to step 902), etc.

At step 948 of method 900, rather than restarting the search space evaluation or a portion thereof, the user may select one or more top predictive model candidates. At step 950, predictive modeling system 600 may present the results of the holdout test for the selected predictive model candidate(s). The holdout test results may provide a final gauge of how these candidates compare. In some embodiments, only users with adequate privileges may release the holdout test results. Preventing the release of the holdout test results until the candidate predictive models are selected may facilitate an unbiased evaluation of performance. However, the exploration engine 610 may actually calculate the holdout test results during the modeling job execution process (e.g., steps 932-944), as long as the results remain hidden until after the candidate predictive models are selected.

Returning to FIG. 10, the user interface 1020 may provide tools for monitoring and/or guiding the search of the predictive modeling space. These tools may provide insight into a prediction problem's dataset (e.g., by highlighting problematic variables in the dataset, identifying relationships between variables in the dataset, etc.), and/or insights into the results of the search. In some embodiments, data analysts may use the interface to guide the search, e.g., by specifying the metrics to be used to evaluate and compare modeling solutions, by specifying the criteria for recognizing a suitable modeling solution, etc. Thus, the user interface may be used by analysts to improve their own productivity, and/or to improve the performance of the exploration engine 610. In some embodiments, user interface 1020 presents the results of the search in real-time, and permits users to guide the search (e.g., to adjust the scope of the search or the allocation of resources among the evaluations of different modeling solutions) in real-time. In some embodiments, user interface 1020 provides tools for coordinating the efforts of multiple data analysts working on the same prediction problem and/or related prediction problems.

In some embodiments, the user interface 1020 provides tools for developing machine-executable templates for the library 630 of modeling techniques. System users may use these tools to modify existing templates, to create new templates, or to remove templates from the library 630. In this way, system users may update the library 630 to reflect advances in predictive modeling research, and/or to include proprietary predictive modeling techniques.

User interface 1020 may include a variety of interface components that allow users to manage multiple modeling projects within an organization, create and modify elements of the modeling methodology hierarchy, conduct comprehensive searches for accurate predictive models, gain insights into the dataset and model results, and/or deploy completed models to produce predictions on new data.

In some embodiments, the user interface 1020 distinguishes between four types of users: administrators, technique developers, model builders, and observers. Administrators may control the allocation of human and computing resources to projects. Technique developers may create and modify modeling techniques and their component tasks. Model builders primarily focus on searching for good models, though they may also make minor adjustments to techniques and tasks. Observers may view certain aspects of project progress and modelling results, but may be prohibited from making any changes to data or initiating any model-building. An individual may fulfill more than one role on a specific project or across multiple projects.

Users acting as administrators may access the project management components of user interface 1020 to set project parameters, assign project responsibilities to users, and allocate computing resources to projects. In some embodiments, administrators may use the project management components to organize multiple projects into groups or hierarchies. All projects within a group may inherit the group's settings. In a hierarchy, all children of a project may inherit the project's settings. In some embodiments, users with sufficient permissions may override inherited settings. In some embodiments, users with sufficient permissions may further divide settings into different sections so that only users with the corresponding permissions may alter them. In some cases, administrators may permit access to certain resources orthogonally to the organization of projects. For example, certain techniques and tasks may be made available to every project unless explicitly prohibited. Others may be prohibited to every project unless explicitly allowed. Moreover, some resources may be allocated on a user basis, so that a project can only access the resources if a user who possesses those rights is assigned to that particular project.

In managing users, administrators may control the group of all users admitted to the system, their permitted roles, and system-level permissions. In some embodiments, administrators may add users to the system by adding them to a corresponding group and issuing them some form of access credentials. In some embodiments, user interface 620 may support different kinds of credentials including, without limitation, username plus password, unified authorization frameworks (e.g., OAuth), hardware tokens (e.g., smart cards), etc.

Once admitted, an administrator may specify that certain users have default roles that they assume for any project. For example, a particular user may be designated as an observer unless specifically authorized for another role by an administrator for a particular project. Another user may be provisioned as a technique developer for all projects unless specifically excluded by an administrator, while another may be provisioned as a technique developer for only a particular group of projects or branch of the project hierarchy. In addition to default roles, administrators may further assign users more specific permissions at the system level. For example, some Administrators may be able to grant access to certain types of computing resources, some technique developers and model builders may be able to access certain features within the builders; and some model builders may be authorized to start new projects, consume more than a given level of computation resources, or invite new users to projects that they do not own.

In some embodiments, administrators may assign access, permissions, and responsibilities at the project level. Access may include the ability to access any information within a particular project. Permissions may include the ability to perform specific operations for a project. Access and permissions may override system-level permissions or provide more granular control. As an example of the former, a user who normally has full builder permissions may be restricted to partial builder permissions for a particular project. As an example of the latter, certain users may be limited from loading new data to an existing project. Responsibilities may include action items that a user is expected to complete for the project.

Users acting as developers may access the builder areas of the interface to create and modify modeling methodologies, techniques, and tasks. As discussed previously, each builder may present one or more tools with different types of user interfaces that perform the corresponding logical operations. In some embodiments, the user interface 1020 may permit developers to use a “Properties” sheet to edit the metadata attached to a technique. A technique may also have tuning parameters corresponding to variables for particular tasks. A developer may publish these tuning parameters to the technique-level Properties sheet, specifying default values and whether or not model builders may override these defaults.

In some embodiments, the user interface 1020 may offer a graphical flow-diagram tool for specifying a hierarchical directed graph of tasks, along with any built-in operations for conditional logic, filtering output, transforming output, partitioning output, combining inputs, iterating over sub-graphs, etc. In some embodiments, user interface 1020 may provide facilities for creating the wrappers around pre-existing software to implement leaf-level tasks, including properties that can be set for each task.

In some embodiments, user interface 1020 may provide advanced developers built-in access to interactive development environments (IDEs) for implementing leaf-level tasks. While developers may, alternatively, code a component in an external environment and wrap that code as a leaf-level task, it may be more convenient if these environments are directly accessible. In such an embodiment, the IDEs themselves may be wrapped in the interface and logically integrated into the task builder. From the user perspective, an IDE may run within the same interface framework and on the same computational infrastructure as the task builder. This capability may enable advanced developers to more quickly iterate in developing and modifying techniques. Some embodiments may further provide code collaboration features that facilitate coordination between multiple developers simultaneously programming the same leaf-level tasks.

Model builders may leverage the techniques produced by developers to build predictive models for their specific datasets. Different model builders may have different levels of experience and thus require different support from the user interface. For relatively new users, the user interface 1020 may present as automatic a process as possible, but still give users the ability to explore options and thereby learn more about predictive modeling. For intermediate users, the user interface 1020 may present information to facilitate rapidly assessing how easy a particular problem will be to solve, comparing how their existing predictive models stack up to what the predictive modeling system 600 can produce automatically, and getting an accelerated start on complicated projects that will eventually benefit from substantial hands-on tuning. For advanced users, the user interface 1020 may facilitate extraction of a few extra decimal places of accuracy for an existing predictive model, rapid assessment of applicability of new techniques to the problems they've worked on, and development of techniques for a whole class of problems their organizations may face. By capturing the knowledge of advanced users, some embodiments facilitate the propagation of that knowledge throughout the rest of the organization.

To support this breadth of user requirements, some embodiments of user interface 1020 provide a sequence of interface tools that reflect the model building process. Moreover, each tool may offer a spectrum of features from basic to advanced. The first step in the model building process may involve loading and preparing a dataset. As discussed previously, a user may upload a file or specify how to access data from an online system. In the context of modeling project groups or hierarchies, a user may also specify what parts of the parent dataset are to be used for the current project and what parts are to be added.

For basic users, predictive modeling system 600 may immediately proceed to building models after the dataset is specified, pausing only if the user interface 1020 flags troubling issues, including, without limitation, unparseable data, too few observations to expect good results, too many observations to execute in a reasonable amount time, too many missing values, or variables whose distributions may lead to unusual results. For intermediate users, user interface 1020 may facilitate understanding the data in more depth by presenting the table of data set characteristics and the graphs of variable importance, variable effects, and effect hotspots. User interface 1020 may also facilitate understanding and visualization of relationships between the variables by providing visualization tools including, without limitation, correlation matrixes, partial dependence plots, and/or the results of unsupervised machine-learning algorithms such as k-means and hierarchical clustering. In some embodiments, user interface 1020 permits advanced users to create entirely new dataset features by specifying formulas that transform an existing feature or combination of them.

Once the dataset is loaded, users may specify the model-fit metric to be optimized. For basic users, predictive modeling system 600 may choose the model-fit metric, and user interface 1020 may present an explanation of the choice. For intermediate users, user interface 1020 may present information to help the users understand the tradeoffs in choosing different metrics for a particular dataset. For advanced users, user interface 620 may permit the user to specify custom metrics by writing formulas (e.g., objective functions) based on the low-level performance data collected by the exploration engine 610 or even by uploading custom metric calculation code.

With the dataset loaded and model-fit metric selected, the user may launch the exploration engine. For basic users, the exploration engine 610 may use the default prioritization settings for modeling techniques, and user interface 620 may provide high-level information about model performance, how far into the dataset the execution has progressed, and the general consumption of computing resources. For intermediate users, user interface 620 may permit the user to specify a subset of techniques to consider and slightly adjust some of the initial priorities. In some embodiments, user interface 620 provides more granular performance and progress data so intermediate users can make in-flight adjustments as previously described. In some embodiments, user interface 620 provides intermediate users with more insight into and control of computing resource consumption. In some embodiments, user interface 620 may provide advanced users with significant (e.g., complete) control of the techniques considered and their priority, all the performance data available, and significant (e.g., complete) control of resource consumption. By either offering distinct interfaces to different levels of users or “collapsing” more advanced features for less advanced users by default, some embodiments of user interface 620 can support the users at their corresponding levels.

During and after the exploration of the search space, the user interface may present information about the performance of one or more modeling techniques. Some performance information may be displayed in a tabular format, while other performance information may be displayed in a graphical format. For example, information presented in tabular format may include, without limitation, comparisons of model performance by technique, fraction of data evaluated, technique properties, or the current consumption of computing resources. Information presented in graphical format may include, without limitation, the directed graph of tasks in a modeling procedure, comparisons of model performance across different partitions of the dataset, representations of model performance such as the receiver operating characteristics and lift chart, predicted vs. actual values, and the consumption of computing resources over time. The user interface 620 may include a modular user interface framework that allows for the easy inclusion of new performance information of either type. Moreover, some embodiments may allow the display of some types of information for each data partition and/or for each technique.

As discussed previously, some embodiments of user interface 620 support collaboration of multiple users on multiple projects. Across projects, user interface 620 may permit users to share data, modeling tasks, and modeling techniques. Within a project, user interface 620 may permit users to share data, models, and results. In some embodiments, user interface 620 may permit users to modify properties of the project and use resources allocated to the project. In some embodiments, user interface 620 may permit multiple users to modify project data and add models to the project, then compare these contributions. In some embodiments, user interface 620 may identify which user made a specific change to the project, when the change was made, and what project resources a user has used.

The model deployment engine 640 provides tools for deploying predictive models in operational environments. In some embodiments, the model deployment engine 640 monitors the performance of deployed predictive models, and updates the performance metadata associated with the modeling techniques that generated the deployed models, so that the performance data accurately reflects the performance of the deployed models.

Users may deploy a fitted prediction model when they believe the fitted model warrants field testing or is capable of adding value. In some embodiments, users and external systems may access a prediction module (e.g., in an interface services layer of predictive modeling system 600), specify one or more predictive models to be used, and supply new observations. The prediction module may then return the predictions provided by those models. In some embodiments, administrators may control which users and external systems have access to this prediction module, and/or set usage restrictions such as the number of predictions allowed per unit time.

For each model, exploration engine 610 may store a record of the modeling technique used to generate the model and the state of model the after fitting, including coefficient and hyper-parameter values. Because each technique is already machine-executable, these values may be sufficient for the execution engine to generate predictions on new observation data. In some embodiments, a model's prediction may be generated by applying the pre-processing and modeling steps described in the modeling technique to each instance of new input data. However, in some cases, it may be possible to increase the speed of future prediction calculations. For example, a fitted model may make several independent checks of a particular variable's value. Combining some or all of these checks and then simply referencing them when convenient may decrease the total amount of computation used to generate a prediction. Similarly, several component models of a blended model may perform the same data transformation. Some embodiments may therefore reduce computation time by identifying duplicative calculations, performing them only once, and referencing the results of the calculations in the component models that use them.

In some embodiments, deployment engine 640 improves the performance of a prediction model by identifying opportunities for parallel processing, thereby decreasing the response time in making each prediction when the underlying hardware can execute multiple instructions in parallel. Some modeling techniques may describe a series of steps sequentially, but in fact some of the steps may be logically independent. By examining the data flow among each step, the deployment engine 640 may identify situations of logical independence and then restructure the execution of predictive models so independent steps are executed in parallel. Blended models may present a special class of parallelization, because the constituent predictive models may be executed in parallel, once any common data transformations have completed.

In some embodiments, deployment engine 640 may cache the state of a predictive model in memory. With this approach, successive prediction requests of the same model may not incur the time to load the model state. Caching may work especially well in cases where there are many requests for predictions on a relatively small number of observations and therefore this loading time is potentially a large part of the total execution time.

In some embodiments, deployment engine 640 may offer at least two implementations of predictive models: service-based and code-based. For service-based prediction, calculations run within a distributed computing infrastructure as described below. Final prediction models may be stored in the data services layer of the distributed computing infrastructure. When a user or external system requests a prediction, it may indicate which model is to be used and provides at least one new observation. A prediction module may then load the model from the data services layer or from the module's in-memory cache, validate that the submitted observations matches the structure of the original dataset, and compute the predicted value for each observation. In some implementations, the predictive models may execute on a dedicated pool of cloud workers, thereby facilitating the generation of predictions with low-variance response times.

Service-based prediction may occur either interactively or via API. For interactive predictions, the user may enter the values of features for each new observation or upload a file containing the data for one or more observations. The user may then receive the predictions directly through the user interface 620, or download them as a file. For API predictions, an external system may access the prediction module via local or remote API, submit one or more observations, and receive the corresponding calculated predictions in return.

Some implementations of deployment engine 640 may allow an organization to create one or more miniaturized instances of the distributed computing infrastructure for the purpose of performing service-based prediction. In the distributed computing infrastructure's interface layer, each such instance may use the parts of the monitoring and prediction modules accessible by external systems, without accessing the user-related functions. The analytic services layer may not use the technique IDE module, and the rest of the modules in this layer may be stripped down and optimized for servicing prediction requests. The data services layer may not use the user or model-building data management. Such standalone prediction instances may be deployed on a parallel pool of cloud resources, distributed to other physical locations, or even downloaded to one or more dedicated machines that act as “prediction appliances”.

To create a dedicated prediction instance, a user may specify the target computing infrastructure, for example, whether it's a set of cloud instances or a set of dedicated hardware. The corresponding modules may then be provisioned and either installed on the target computing infrastructure or packaged for installation. The user may either configure the instance with an initial set of predictive models or create a “blank” instance. After initial installation, users may manage the available predictive models by installing new ones or updating existing ones from the main installation.

For code-based predictions, the deployment engine 640 may generate source code for calculating predictions based on a particular model, and the user may incorporate the source code into other software. When models are based on techniques whose leaf-level tasks are all implemented in the same programming language as that requested by the user, deployment engine 640 may produce the source code for the predictive model by collating the code for leaf-level tasks. When the model incorporates code from different languages or the language is different from that desired by the user, deployment engine 640 may use more sophisticated approaches.

One approach is to use a source-to-source compiler to translate the source code of the leaf-level tasks into a target language. Another approach is to generate a function stub in the target language that then calls linked-in object code in the original language or accesses an emulator running such object code. The former approach may involve the use of a cross-compiler to generate object code specifically for the user's target computing platform. The latter approach may involve the use of an emulator that will run on the user's target platform.

Another approach is to generate an abstract description of a particular model and then compile that description into the target language. To generate an abstract description, some embodiments of deployment engine 640 may use meta-models for describing a large number of potential pre-processing, model-fitting, and post-processing steps. The deployment engine may then extract the particular operations for a complete model and encode them using the meta-model. In such embodiments, a compiler for the target programming language may be used to translate the meta-models into the target language. So if a user wants prediction code in a supported language, the compiler may produce it. For example, in a decision-tree model, the decisions in the tree may be abstracted into logical if/then/else statements that are directly implementable in a wide variety of programming languages. Similarly, a set of mathematical operations that are supported in common programming languages may be used to implement a linear regression model.

However, disclosing a predictive model's source code in any language may be undesirable in some cases (e.g., in cases where the predictive modeling technique or predictive model contains proprietary capabilities or information). Therefore, the deployment engine 640 may convert a predictive model into a set of rules that preserves the predictive capabilities of the predictive model without disclosing its procedural details. One approach is to apply an algorithm that produces such rules from a set of hypothetical predictions that a predictive model would generate in response to hypothetical observations. Some such algorithms may produce a set of if-then rules for making predictions. For these algorithms, the deployment engine 640 may then convert the resulting if-then rules into a target language instead of converting the original predictive model. An additional advantage of converting a predictive model to a set of if-then rules is that it is generally easier to convert a set of if-then rules into a target programming language than a predictive model with arbitrary control and data flows because the basic model of conditional logic is more similar across programming languages.

Once a model starts making predictions on new observations, the deployment engine 640 may track these predictions, measure their accuracy, and use these results to improve predictive modeling system 600. In the case of service-based predictions, because predictions occur within the same distributed computing environment as the rest of the system, each observation and prediction may be saved via the data services layer. By providing an identifier for each prediction, some embodiments may allow a user or external software system to submit the actual values, if and when they are recorded. In the case of code-based predictions, some embodiments may include code that saves observations and predictions in a local system or back to an instance of the data services layer. Again, providing an identifier for each prediction may facilitate the collection of model performance data against the actual target values when they become available.

Information collected directly by the deployment engine 640 about the accuracy of predictions, and/or observations obtained through other channels, may be used to improve the model for a prediction problem (e.g., to “refresh” an existing model, or to generate a model by re-exploring the modeling search space in part or in full). New data can be added to improve a model in the same ways data was originally added to create the model, or by submitting target values for data previously used in prediction.

Some models may be refreshed (e.g., refitted) by applying the corresponding modeling techniques to the new data and combining the resulting new model with the existing model, while others may be refreshed by applying the corresponding modeling techniques to a combination of original and new data. In some embodiments, when refreshing a model, only some of the model parameters may be recalculated (e.g., to refresh the model more quickly, or because the new data provides information that is particularly relevant to particular parameters).

Alternatively or in addition, new models may be generated exploring the modeling search space, in part or in full, with the new data included in the dataset. The re-exploration of the search space may be limited to a portion of the search space (e.g., limited to modeling techniques that performed well in the original search), or may cover the entire search space. In either case, the initial suitability scores for the modeling technique(s) that generated the deployed model(s) may be recalculated to reflect the performance of the deployed model(s) on the prediction problem. Users may choose to exclude some of the previous data to perform the recalculation. Some embodiments of deployment engine 640 may track different versions of the same logical model, including which subsets of data were used to train which versions.

In some embodiments, this prediction data may be used to perform post-request analysis of trends in input parameters or predictions themselves over time, and to alert the user of potential issues with inputs or the quality of the model predictions. For example, if an aggregate measure of model performance starts to degrade over time, the system may alert the user to consider refreshing the model or investigating whether the inputs themselves are shifting. Such shifts may be caused by temporal change in a particular variable or drifts in the entire population. In some embodiments, most of this analysis is performed after prediction requests are completed, to avoid slowing down the prediction responses. However, the system may perform some validation at prediction time to avoid particularly bad predictions (e.g., in cases where an input value is outside a range of values that it has computed as valid given characteristics of the original training data, modeling technique, and final model fitting state).

After-the-fact analysis may be done in cases where a user has deployed a model to make extrapolations well beyond the population used in training. For example, a model may have been trained on data from one geographic region, but used to make predictions for a population in a completely different geographic region. Sometimes, such extrapolation to new populations may result in model performance that is substantially worse than expected. In these cases, the deployment engine 640 may alert the user and/or automatically refresh the model by re-fitting one or more modeling techniques using the new values to extend the original training data.

The predictive modeling system 600 may significantly improve the productivity of analysts at any skill level and/or significantly increase the accuracy of predictive models achievable with a given amount of resources. Automating procedures can reduce workload and systematizing processes can enforce consistency, enabling analysts to spend more time generating unique insights. Three common scenarios illustrate these advantages: forecasting outcomes, predicting properties, and inferring measurements.

Forecasting Outcomes

If an organization can accurately forecast outcomes, then it can both plan more effectively and enhance its behavior. Therefore, a common application of machine learning is to develop algorithms that produce forecasts. For example, many industries face the problem of predicting costs in large-scale, time-consuming projects.

In some embodiments, the techniques described herein can be used for forecasting cost overruns (e.g., software cost overruns or construction cost overruns). For example, the techniques described herein may be applied to the problem of forecasting cost overruns as follows:

1. Select a model fitting metric appropriate to the response variable type (e.g., numerical or binary, approximately Gaussian or strongly non-Gaussian): Predictive modeling system 600 may recommend a metric based on data characteristics, requiring less skill and effort by the user, but allows the user to make the final selection.

2. Pre-treat the data to address outliers and missing data values: Predictive modeling system 600 may provide detailed summary of data characteristics, enabling users to develop better situational awareness of the modeling problem and assess potential modeling challenges more effectively. Predictive modeling system 600 may include automated procedures for outlier detection and replacement, missing value imputation, and the detection and treatment of other data anomalies, requiring less skill and effort by the user. The predictive modeling system's procedures for addressing these challenges may be systematic, leading to more consistent modeling results across methods, datasets, and time than ad hoc data editing procedures.

3. Partition the data for modeling and evaluation: The predictive modeling system 600 may automatically partition data into training, validation, and holdout sets. This partitioning may be more flexible than the train and test partitioning used by some data analysts, and consistent with widely accepted recommendations from the machine learning community. The use of a consistent partitioning approach across methods, datasets, and time can make results more comparable, enabling more effective allocation of deployment resources in commercial contexts.

4. Select model structures, generate derived features, select model tuning parameters, fit models, and evaluate: In some embodiments, the predictive modeling system 600 can fit many different model types, including, without limitation, decision trees, neural networks, support vector machine models, regression models, boosted trees, random forests, deep learning neural networks, etc. The predictive modeling system 600 may provide the option of automatically constructing ensembles from those component models that exhibit the best individual performance. Exploring a larger space of potential models can improve accuracy. The predictive modeling system may automatically generate a variety of derived features appropriate to different data types (e.g., Box-Cox transformations, text pre-processing, principal components, etc.). Exploring a larger space of potential transformation can improve accuracy. The predictive modeling system 600 may use cross validation to select the best values for these tuning parameters as part of the model building process, thereby improving the choice of tuning parameters and creating an audit trail of how the selection of parameters affects the results. The predictive modeling system 600 may fit and evaluate the different model structures considered as part of this automated process, ranking the results in terms of validation set performance.

5. Select the final model: The choice of the final model can be made by the predictive modeling system 600 or by the user. In the latter case, the predictive modeling system may provide support to help the user make this decision, including, for example, the ranked validation set performance assessments for the models, the option of comparing and ranking performance by other quality measures than the one used in the fitting process, and/or the opportunity to build ensemble models from those component models that exhibit the best individual performance.

A practical aspect of the predictive modeling system's model development process is that, once the initial dataset has been assembled, all subsequent computations may occur within the same software environment. This aspect represents a difference from the conventional model-building efforts, which often involves a combination of different software environments. A practical disadvantage of such multi-platform analysis approaches is the need to convert results into common data formats that can be shared between the different software environments. Often this conversion is done either manually or with custom “one-off” reformatting scripts. Errors in this process can lead to extremely serious data distortions. Predictive modeling system 600 may avoid such reformatting and data transfer errors by performing all computations in one software environment. More generally, because it is highly automated, fitting and optimizing many different model structures, the predictive modeling system 600 can provide a substantially faster and more systematic, thus more readily explainable and more repeatable, route to the final model. Moreover, as a consequence of the predictive modeling system 600 exploring more different modeling methods and including more possible predictors, the resulting models may be more accurate than those obtained by traditional methods.

I. Predicting Properties

In many fields, organizations face uncertainty in the outcome of a production process and want to predict how a given set of conditions will affect the final properties of the output. Therefore, a common application of machine learning is to develop algorithms that predict these properties. For example, concrete is a common building material whose final structural properties can vary dramatically from one situation to another. Due to the significant variations in concrete properties with time and their dependence on its highly variable composition, neither models developed from first principles nor traditional regression models offer adequate predictive accuracy.

In some embodiments, the techniques described herein can be used for predicting properties of the outcome of a production process (e.g., properties of concrete). For example, the techniques described herein may be applied to the problem of predicting properties of concrete as follows:

1. Partition the dataset into training, validation, and test subsets.

2. Clean the modeling dataset: The predictive modeling system 600 may automatically check for missing data, outliers, and other data anomalies, recommending treatment strategies and offering the user the option to accept or decline them. This approach may require less skill and effort by the user, and/or may provide more consistent results across methods, datasets, and time.

3. Select the response variable and choose a primary fitting metric: The user may select the response variable to be predicted from those available in the modeling dataset. Once the response variable has been chosen, the predictive modeling system 600 may recommend a compatible fitting metric, which the user may accept or override. This approach may require less skill and effort by the user. Based on the response variable type and the fitting metric selected, the predictive modeling system may offer a set of predictive models, including traditional regression models, neural networks, and other machine learning models (e.g., random forests, boosted trees, support vector machines). By automatically searching among the space of possible modeling approaches, the predictive modeling system 600 may increase the expected accuracy of the final model. The default set of model choices may be overridden to exclude certain model types from consideration, to add other model types supported by the predictive modeling system but not part of the default list, or to add the user's own custom model types (e.g., implemented in R or Python).

4. Generate input features, fit models, optimize model-specific tuning parameters, and evaluate performance: In some embodiments, feature generating may include scaling for numerical covariates, Box-Cox transformations, principal components, etc. Tuning parameters for the models may be optimized via cross-validation. Validation set performance measures may be computed and presented for each model, along with other summary characteristics (e.g., model parameters for regression models, variable importance measures for boosted trees or random forests).

5. Select the final model: The choice of the final model can be made by the predictive modeling system 600 or by the user. In the latter case, the predictive modeling system may provide support to help the user make this decision, including, for example, the ranked validation set performance assessments for the models, the option of comparing and ranking performance by other quality measures than the one used in the fitting process, and/or the opportunity to build ensemble models from those component models that exhibit the best individual performance.

J. Inferring Measurements

Some measurements are much more costly to make than others, so organizations may want to substitute cheaper metrics for more expensive ones. Therefore, a common application of machine learning is to infer the likely output of an expensive measurement from the known output of cheaper ones. For example, “curl” is a property that captures how paper products tend to depart from a flat shape, but it can typically be judged only after products are completed. Being able to infer the curl of paper from mechanical properties easily measured during manufacturing can thus result in an enormous cost savings in achieving a given level of quality. For typical end-use properties, the relationship between these properties and manufacturing process conditions is not well understood.

In some embodiments, the techniques described herein can be used for inferring measurements. For example, the techniques described herein may be applied to the problem of inferring measurements as follows:

1. Characterize the modeling datasets: The predictive modeling system 600 may provide key summary characteristics and offer recommendations for treatment of data anomalies, which the user is free to accept, decline, or request more information about. For example, key characteristics of variables may be computed and displayed, the prevalence of missing data may be displayed and a treatment strategy may be recommended, outliers in numerical variables may be detected and, if found, a treatment strategy may be recommended, and/or other data anomalies may be detected automatically (e.g., inliers, non-informative variables whose values never change) and recommended treatments may be made available to the user.

2. Partition the dataset into training/validation/holdout subsets.

3. Feature generation/model structure selection/model fitting: The predictive modeling system 600 may combine and automate these steps, allowing extensive internal iteration. Multiple features may be automatically generated and evaluated, using both classical techniques like principal components and newer methods like boosted trees. Many different model types may be fitted and compared, including regression models, neural networks, support vector machines, random forests, boosted trees, and others. In addition, the user may have the option of including other model structures that are not part of this default collection. Model sub-structure selection (e.g., selection of the number of hidden units in neural networks, the specification of other model-specific tuning parameters, etc.) may be automatically performed by extensive cross-validation as part of this model fitting and evaluation process.

4. Select the final model: The choice of the final model can be made by the predictive modeling system 600 or by the user. In the latter case, the predictive modeling system may provide support to help the user make this decision, including, for example, the ranked validation set performance assessments for the models, the option of comparing and ranking performance by other quality measures than the one used in the fitting process, and/or the opportunity to build ensemble models from those component models that exhibit the best individual performance.

In some embodiments, because the predictive modeling system 600 automates and efficiently implements data pretreatment (e.g., anomaly detection), data partitioning, multiple feature generation, model fitting and model evaluation, the time required to develop models may be much shorter than it is in the traditional development cycle. Further, in some embodiments, because the predictive modeling system automatically includes data pretreatment procedures to handle both well-known data anomalies like missing data and outliers, and less widely appreciated anomalies like inliers (repeated observations that are consistent with the data distribution, but erroneous) and postdictors (e.g., extremely predictive covariates that arise from information leakage), the resulting models may be more accurate and more useful. In some embodiments, the predictive modeling system 600 is able to explore a vastly wider range of model types, and many more specific models of each type, than is traditionally feasible. This model variety may greatly reduce the likelihood of unsatisfactory results, even when applied to a dataset of compromised quality.

Referring to FIG. 10, in some embodiments, a predictive modeling system 1000 (e.g., an embodiment of predictive modeling system 600) includes at least one client computer 1010, at least one server 1050, and one or more processing nodes 1070. The illustrative configuration is only for exemplary purposes, and it is intended that there can be any number of clients 1010 and/or servers 1050.

In some embodiments, predictive modeling system 1000 may perform one or more (e.g., all) steps of method 800. In some embodiments, client 1010 may implement the user interface 1020, and the predictive modeling module 1052 of server 1050 may implement other components of predictive modeling system 600 (e.g., modeling space exploration engine 610, library of modeling techniques 630, a library of prediction problems, and/or modeling deployment engine 640). In some embodiments, the computational resources allocated by exploration engine 610 for the exploration of the modeling search space may be resources of the one or more processing nodes 1070, and the one or more processing nodes 1070 may execute the modeling techniques according to the resource allocation schedule. However, embodiments are not limited by the manner in which the components of predictive modeling system 600 or predictive modeling method 800 are distributed between client 1010, server 1050, and one or more processing nodes 1070. Furthermore, in some embodiments, all components of predictive modeling system 600 may be implemented on a single computer (instead of being distributed between client 1010, server 1050, and processing node(s) 1070), or implemented on two computers (e.g., client 1010 and server 1050).

One or more communications networks 1030 connect the client 1010 with the server 1050, and one or more communications networks 1080 connect the server 1050 with the processing node(s) 1070. The communication networks 1030 or 1080 can include one or more component or functionality of network 570. The communication may take place via any media such as standard telephone lines, LAN or WAN links (e.g., T1, T3, 56 kb, X.25), broadband connections (ISDN, Frame Relay, ATM), and/or wireless links (IEEE 802.11, Bluetooth). The networks 1030/1080 can carry TCP/IP protocol communications, and data (e.g., HTTP/HTTPS requests, etc.) transmitted by client 1010, server 1050, and processing node(s) 1070 can be communicated over such TCP/IP networks. The type of network is not a limitation, however, and any suitable network may be used. Non-limiting examples of networks that can serve as or be part of the communications networks 1030/1080 include a wireless or wired Ethernet-based intranet, a local or wide-area network (LAN or WAN), and/or the global communications network known as the Internet, which may accommodate many different communications media and protocols.

The client 1010 can be implemented with software 1012 running on hardware. In some embodiments, the hardware may include a personal capable of running operating systems and/or various varieties of Unix and GNU/Linux. The client 1010 may also be implemented on such hardware as a smart or dumb terminal, network computer, wireless device, wireless telephone, information appliance, workstation, minicomputer, mainframe computer, personal data assistant, tablet, smart phone, or other computing device that is operated as a general purpose computer, or a special purpose hardware device used solely for serving as a client 1010.

Generally, in some embodiments, clients 1010 can be operated and used for various activities including sending and receiving electronic mail and/or instant messages, requesting and viewing content available over the World Wide Web, participating in chat rooms, or performing other tasks commonly done using a computer, handheld device, or cellular telephone. Clients 1010 can also be operated by users on behalf of others, such as employers, who provide the clients 1010 to the users as part of their employment.

In various embodiments, the software 1012 of client computer 610 includes client software 1014 and/or a web browser 1016. The web browser 1016 allows the client 1010 to request a web page or other downloadable program, applet, or document (e.g., from the server 1050) with a web-page request. One example of a web page is a data file that includes computer executable or interpretable information, graphics, sound, text, and/or video, that can be displayed, executed, played, processed, streamed, and/or stored and that can contain links, or pointers, to other web pages.

In some embodiments, the software 1012 includes client software 1014. The client software 1014 provides, for example, functionality to the client 1010 that allows a user to send and receive electronic mail, instant messages, telephone calls, video messages, streaming audio or video, or other content. Not shown are standard components associated with client computers, including a central processing unit, volatile and non-volatile storage, input/output devices, and a display.

In some embodiments, web browser software 1016 and/or client software 1014 may allow the client to access a user interface 1020 for a predictive modeling system 600.

The server 1050 interacts with the client 1010. The server 1050 can be implemented on one or more server-class computers that have sufficient memory, data storage, and processing power and that run a server-class operating system. System hardware and software other than that specifically described herein may also be used, depending on the capacity of the device and the size of the user base. For example, the server 1050 may be or may be part of a logical group of one or more servers such as a server farm or server network. As another example, there may be multiple servers 1050 associated with or connected to each other, or multiple servers may operate independently, but with shared data. In a further embodiment and as is typical in large-scale systems, application software can be implemented in components, with different components running on different server computers, on the same server, or some combination.

In some embodiments, server 1050 includes a predictive modeling module 1052, a communications module 1056, and/or a data storage module 1054. In some embodiments, the predictive modeling module 1052 may implement modeling space exploration engine 610, library of modeling techniques 630, a library of prediction problems, and/or modeling deployment engine 640. In some embodiments, server 1050 may use communications module 1056 to communicate the outputs of the predictive modeling module 1052 to the client 1010, and/or to oversee execution of modeling techniques on processing node(s) 1070. The modules described throughout the specification can be implemented in whole or in part as a software program using any suitable programming language or languages (C++, C#, java, LISP, BASIC, PERL, etc.) and/or as a hardware device (e.g., ASIC, FPGA, processor, memory, storage and the like).

A data storage module 1054 may store, for example, predictive modeling library 630 and/or a library of prediction problems.

FIG. 7 illustrates an implementation of a predictive modeling system 600. The discussion of FIG. 7 is given by way of example of some embodiments, and is in no way limiting.

To execute the previously described procedures, predictive modeling system 600 may use a distributed software architecture 1100 running on a variety of client and server computers. The goal of the software architecture 1100 is to simultaneously deliver a rich user experience and computationally intensive processing. The software architecture 1100 may implement a variation of the basic 4-tier Internet architecture. As illustrated in FIG. 11, it extends this foundation to leverage cloud-based computation, coordinated via the application and data tiers.

The similarities and differences between architecture 1100 and the basic 4-tier Internet architecture may include:

(1) Clients 1110. The architecture 1100 makes essentially the same assumptions about clients 1110 as any other Internet application. The primary use-case includes frequent access for long periods of time to perform complex tasks. So target platforms include rich Web clients running on a laptop or desktop. However, users may access the architecture via mobile devices. Therefore, the architecture is designed to accommodate native clients 712 directly accessing the Interface Services APIs using relatively thin client-side libraries. Of course, any cross-platform GUI layers such as Java and Flash, could similarly access these APIs.

(2) Interface Services 1120. This layer of the architecture is an extended version of the basic Internet presentation layer. Due to the sophisticated user interaction that may be used to direct machine learning, alternative implementations may support a wide variety of content via this layer, including static HTML, dynamic HTML, SVG visualizations, executable Javascript code, and even self-contained IDEs. Moreover, as new Internet technologies evolve, implementations may need to accommodate new forms of content or alter the division of labor between client, presentation, and application layers for executing user interaction logic. Therefore, their Interface Services layer 1120 may provide a flexible framework for integrating multiple content delivery mechanisms of varying richness, plus common supporting facilities such as authentication, access control, and input validation.

(3) Analytic Services 1130. The architecture may be used to produce predictive analytics solutions, so its application tier focuses on delivering Analytic Services. The computational intensity of machine learning drives the primary enhancement to the standard application tier—the dynamic allocation of machine-learning tasks to large numbers of virtual “workers” running in cloud environments. For every type of logical computation request generated by the execution engine, the Analytic Services layer 1130 coordinates with the other layers to accept requests, break requests into jobs, assign jobs to workers, provide the data necessary for job execution, and collate the execution results. There is also an associated difference from a standard application tier. The predictive modeling system 600 may allow users to develop their own machine-learning techniques and thus some implementations may provide one or more full IDEs, with their capabilities partitioned across the Client, Interface Services, and Analytic Services layers. The execution engine then incorporates new and improved techniques created via these IDEs into future machine-learning computations.

(4) Worker Clouds 1140. To efficiently perform modeling computations, the predictive modeling system 600 may break them into smaller jobs and allocates them to virtual worker instances running in cloud environments. The architecture 700 allows for different types of workers and different types of clouds. Each worker type corresponds to a specific virtual machine configuration. For example, the default worker type provides general machine-learning capabilities for trusted modeling code. But another type enforces additional security “sandboxing” for user-developed code. Alternative types might offer configurations optimized for specific machine-learning techniques. As long as the Analytic Services layer 1130 understands the purpose of each worker type, it can allocate jobs appropriately. Similarly, the Analytic Services layer 1130 can manage workers in different types of clouds. An organization might maintain a pool of instances in its private cloud as well as have the option to run instances in a public cloud. It might even have different pools of instances running on different kinds of commercial cloud services or even a proprietary internal one. As long as the Analytic Services layer 730 understands the tradeoffs in capabilities and costs, it can allocate jobs appropriately.

(5) Data Services 1150. The architecture 1100 assumes that the various services running in the various layers may benefit from a corresponding variety of storage options. Therefore, it provides a framework for delivering a rich array of Data Services 1150, e.g., file storage for any type of permanent data, temporary databases for purposes such as caching, and permanent databases for long-term record management. Such services may even be specialized for particular types of content such as the virtual machine image files used for cloud workers and IDE servers. In some cases, implementations of the Data Services layer 1150 may enforce particular access idioms on specific types of data so that the other layers can smoothly coordinate. For instance, standardizing the format for datasets and model results means the Analytic Services layer 1130 may simply pass a reference to a user's dataset when it assigns a job to a worker. Then, the worker can access this dataset from the Data Services layer 1150 and return references to the model results which it has, in turn, stored via Data Services 1150.

(6) External Systems 1160. Like any other Internet application, the use of APIs may enable external systems to integrate with the predictive modeling system 600 at any layer of the architecture 1100. For example, a business dashboard application could access graphic visualizations and modeling results through the Interface Services layer 1120. An external data warehouse or even live business application could provide modeling datasets to the Analytic Services layer 1130 through a data integration platform. A reporting application could access all the modeling results from a particular time period through the Data Services layer 1150. However, under most circumstances, external systems would not have direct access to Worker Clouds 1140; they would utilize them via the Analytic Services layer 1130.

As with all multi-tiered architectures, the layers of architecture 1100 are logical. Physically, services from different layers could run on the same machine, different modules in the same layer could run on separate machines, and multiple instances of the same module could run across several machines. Similarly, the services in one layer could run across multiple network segments and services from different layers may or may not run on different network segments. But the logical structure helps coordinate developers' and operators' expectations of how different modules will interact, as well as gives operators the flexibility necessary to balance service-level requirements such as scalability, reliability, and security.

While the high-level layers appear reasonably similar to those of a typical Internet application, the addition of cloud-based computation may substantially alter how information flows through the system.

Internet applications usually offer two distinct types of user interaction: synchronous and asynchronous. With conceptually synchronous operations, such as finding an airline flight and booking a reservation, the user makes a request and waits for the response before making the next request. With conceptually asynchronous operations, such as setting an alert for online deals that meet certain criteria, the user makes a request and expects the system to notify him at some later time with results. (Typically, the system provides the user an initial request “ticket” and offers notification through a designated communications channel.)

In contrast, building and refining machine-learning models may involve an interaction pattern somewhere in the middle. Setting up a modeling problem may involve an initial series of conceptually synchronous steps. But when the user instructs the system to begin computing alternative solutions, a user who understands the scale of the corresponding computations is unlikely to expect an immediate response. Superficially, this expectation of delayed results makes this phase of interaction appear asynchronous.

However, predictive modeling system 600 doesn't force the user to “fire-and-forget”, i.e., stop his own engagement with the problem until receiving a notification. In fact, it may encourage him to continue exploring the dataset and review preliminary results as soon as they arrive. Such additional exploration or initial insight might inspire him to change the model-building parameters “in-flight”. The system may then process the requested changes and reallocate processing tasks. The predictive modeling system 600 may allow this request-and-revise dynamic continuously throughout the user's session.

The predictive modeling system 600 may not fit cleanly into the layered model, which assumes that each layer mostly only relies on the layer directly below it. Various analytic services and data services can cooperatively coordinate users and computation.

To make operational predictions, a user may want an independent prediction service, completely separate from the model building computing infrastructure. An independent prediction service may run in a different computing environment or be managed as a distinct component within a shared computing environment. Once instantiated, the service's execution, security, and monitoring may be fully separated from the model building environment allowing the user to deploy and manage it independently.

After instantiating the service, the deployment engine may allow the user to install fitted models into the service. To enhance (e.g., optimize) performance, the implementation of a modeling technique suitable for fitting models may be suboptimal for making predictions. For example, fitting a model requires running the same algorithm repeatedly so it is often worthwhile to invest a significant amount of overhead into enabling fast parallel execution of the algorithm. However, if the expected rate of prediction requests isn't very high, that same overhead may not be worthwhile for an independent prediction service. In some cases, a modeling technique developer may even provide specialized versions of one or more of its component execution tasks that provide better performance characteristics in a prediction environment. In particular, implementations designed for highly parallel execution or execution on specialized processors may be advantageous for prediction performance. Similarly, in cases where a modeling technique includes tasks specified in a programming language, pre-compiling the tasks at the time of service instantiation rather than waiting until service startup or an initial request for a prediction from that model may provide a performance improvement.

Also, model fitting tasks generally use computing infrastructure differently than a prediction service. To protect a cloud infrastructure from errors during modeling technique execution and to prevent access to modeling techniques from other users in the cloud, modeling techniques may execute in secure computing containers during model fitting. However, prediction services often run on dedicated machines or clusters. Removing the secure container layer may therefore reduce overhead without any practical disadvantage.

Therefore, based on the specific tasks executed by a model's modeling technique, the expected load, and the characteristics of the target computing environment for prediction, the deployment engine may use a set of rules for packaging and deploying the model. These rules may optimize execution.

Because a given prediction service may execute multiple models, the service may allocate computing resources across prediction requests for each model. There are two basic cases, deployments to one or more server machines and deployments to computing clusters.

In the case of deployments to servers, the challenge is how to allocate requests among multiple servers. The prediction service may have several types of a priori information. Such information may include (a) estimates of how long it takes to execute a prediction for each configured model, (b) the expected frequency of requests for each configured model at different times, and (c) the desired priority of model execution. Estimates of execution time may be calculated based on measuring the actual execution speed of the prediction code for each model under one or more conditions. The desired priority of model execution may be specified by a service administrator. The expected frequency of requests could be computed from historical data for that model, forecast based on a meta-machine learning model, or provided by an administrator.

The service may include an objective function that combines some or all of these factors to compute a fraction of all available servers' aggregate computing power that may be initially allocated to each model. As the service receives and executes requests, it naturally obtains updated information on estimates of execution time and expected frequency of requests. Therefore, the service may recalculate these fractions and reallocate models to servers accordingly.

A deployed prediction service may have two different types of server processes: routers and workers. One or more routers may form a routing service that accepts requests for predictions and allocates them to workers. Incoming requests may have a model identifier indicating which prediction model to use, a user or client identifier indicating which user or software system is making the request, and one or more vectors of predictor variables for that model.

When a request comes into a dedicated prediction service, its routing service may inspect some combination of the model identifier, user or client identifier, and number of vectors of predictor variables. The routing service may then allocate requests to workers to increase (e.g., maximize) server cache hits for instructions and data used (1) in executing a given model and/or (2) for a given user or client. The routing service may also take into account the number of vectors of predictor variables to achieve a mixture of batch sizes submitted to each worker that balances latency and throughput.

Examples of algorithms for allocating requests for a model across workers may include round-robin, weighted round robin based on model computation intensity and/or computing power of the worker, and dynamic allocation based on reported load. To facilitate quick routing of requests to the designated server, the routing service may use a hash function that chooses the same server given the same set of observed characteristics (e.g., model identifier). The hash function may be a simple hash function or a consistent hash function. A consistent hash function requires less overhead when the number of nodes (corresponding to workers in this case) changes. So if a worker goes down or new workers are added, a consistent hash function can reduce the number of hash keys that must be recomputed.

In addition to enhancing (e.g., optimizing) performance by intelligently distributing prediction requests among available services, a prediction service may enhance (e.g., optimize) the performance of individual models by intelligently configuring how each worker executes each model. For example, if a given server receives a mix of requests for several different models, loading and unloading models for each request may incur substantial overhead. However, aggregating requests for batch processing may incur substantial latency. In some embodiments, the service can intelligently make this tradeoff if the administrator specifies the latency tolerance for a model. For example, urgent requests may have a latency tolerance of only 100 milliseconds in which case a server may process only one or at most a few requests. In contrast, a latency tolerance might of two seconds may enable batch sizes in the hundreds. Due to overhead, increasing the latency tolerance by a factor of two may increase throughput by 10× to 100×.

Similarly, using operating system threads may improve throughput while increasing latency, due to the thread set up and initialization overhead. In some cases, predictions may be extremely latency sensitive. If all the requests to a given model are likely to be latency sensitive, then the service may configure the servers handling those requests to operate in single threaded mode. Also, if only a subset of requests are likely to be latency sensitive, the service may allow requesters to flag a given request as sensitive. In this case, the server may operate in single threaded mode only while servicing the specific request.

In some cases, a user's organization may have batches of predictions that the organization wants to use a distributed computing cluster to calculate as rapidly as possible. Distributed computing frameworks generally allow an organization to set up a cluster running the framework, and any programs designed to work with the framework can then submit jobs comprising data and executable instructions.

Because the execution of one prediction on a model does not affect the result of another prediction on that model, or the result of any other model, predictions are stateless operations in the context of a cluster computing and thus are generally very easy to make parallel. Therefore, given a batch of data and executable instructions, the normal behavior of the framework's partitioning and allocation algorithms may result in linear scaling.

In some cases, making predictions may be part of a large workflow in which data is produced and consumed in many steps. In such cases, prediction jobs may be integrated with other operations through publish-subscribe mechanisms. The prediction service subscribes to channels that produce new observations that require predictions. After the service makes predictions, it publishes them to one or more channels that other programs may consume.

Fitting modeling techniques and/or searching among a large number of alternative techniques can be computationally intensive. Computing resources may be costly. Some embodiments of the system 600 for producing predictive models identifies opportunities to reduce resource consumption.

Based on user preferences, the engine 610 may adjust its search for models to reduce execution time and consumption of computing resources. In some cases, a prediction problem may include a lot of training data. In such cases, the benefit of cross validation is usually lower in terms of reducing model bias. Therefore, the user may prefer to fit a model on all the training data at once rather than on each cross validation fold, because the computation time of one run on five to ten times the amount of data is typically much less than five to 10 runs on one-fifth to one-tenth the amount of data.

Even in cases where a user does not have a relatively large training set, the user may still wish to conserve time and resources. In such cases, the engine 610 may offer a “greedier” option that uses several more aggressive search approaches. First, the engine 610 can try a smaller subset of possible modeling techniques (e.g., only those whose expected performance is relatively high). Second, the engine 610 may prune underperforming models more aggressively in each round of training and evaluation. Third, the engine 610 may take larger steps when searching for the optimal hyper-parameters for each model.

In general, searching for the better (e.g., optimal) hyper-parameters can be costly. So even if the user wants to the engine 610 to evaluate a wide spectrum of potential models and not prune them aggressively, the engine can still conserve resources by limiting (e.g., optimizing) the hyper-parameter search. The cost of this search is generally proportional to the size of the dataset. One strategy is to tune the hyper-parameters on a small fraction of the dataset and then extrapolate these parameters to the entire dataset. In some cases, adjustments are made to account for the larger amount of data. In some embodiments, the engine 610 can use one of two strategies. First, the engine 610 can perform the adjustment based on heuristics for that modeling technique. Second, the engine 610 can engage in meta-machine learning, tracking how each modeling technique's hyper-parameters vary with dataset size and building a meta predictive model of those hyper-parameters, then applying that meta model in cases where the user wants to make the tradeoff.

When working with a categorical prediction problem, there may be a minority class and a majority class. The minority class may be much smaller but relatively more useful, as in the case of fraud detection. In some embodiments, the engine 610 “down-samples” the majority class so that the number of training observations for that class is more similar to that for the minority class. In some cases, modeling techniques may automatically accommodate such weights directly during model fit. If the modeling techniques do not accommodate such weights, the engine 610 can make a post-fit adjustment proportional to the amount of down-sampling. This approach may sacrifice some accuracy for much shorter execution times and lower resource consumption.

Some modeling techniques may execute more efficiently than others. For example, some modeling techniques may be optimized to run on parallel computing clusters or on servers with specialized processors. Each modeling technique's metadata may indicate any such performance advantages. When the engine 610 is assigning computing jobs, it may detect jobs for modeling techniques whose advantages apply in the currently available computing environment. Then, during each round of search, the engine 610 may use bigger chunks of the dataset for those jobs. Those modeling techniques may then complete faster. Moreover, if their accuracy is great enough, there may be no need to even test other modeling techniques that are performing relatively poorly.

K. User Interface (UI) Enhancements

The engine 610 may help users produce better predictive models by extracting more information from them before model building, and may provide users with a better understanding of model performance after model fitting.

In some cases, a user may have additional information about datasets that is suitable for better directing the search for accurate predictive models. For example, a user may know that certain observations have special significance and want to indicate that significance. The engine 610 may allow the user to easily create new variables for this purpose. For example, one synthetic variable may indicate that the engine should use particular observations as part of the training, validation, or holdout data partitions instead of assigning them to such partitions randomly. This capability may be useful in situations where certain values occur infrequently and corresponding observations should be carefully allocated to different partitions. This capability may be useful in situations where the user has trained a model using a different machine learning system and wants to perform a comparison where the training, validation, and holdout partitions are the same.

Similarly, certain observations may represent particularly useful or indicative events to which the user wants to assign additional weight. Thus, an additional variable inserted into the dataset may indicate the relative weight of each observation. The engine 610 may then use this weight when training models and calculating their accuracy, with the goal being to produce more accurate predictions under higher-weighted conditions.

In other cases, the user may have prior information about how certain features should behave in the models. For example, a user may know that a certain feature should have a monotonic effect on the prediction target over a certain range. In automobile insurance, it is generally believed that the chance of accident increases monotonically with age after the age of 30. Another example is creating bands for otherwise continuous variables. Personal income is continuous, but there are analytic conventions for assigning values to bands such as $10K increments up until $100K and then $25K bands until $250K, and any income greater than $250K. Then there are cases where limitations on the dataset require constraints on specific features. Sometimes, categorical variables may have a very large number of values relative to the size of dataset. The user may wish to indicate either that the engine 610 should ignore categorical features that have more than a certain number of possible categories or limit the number of categories to the most frequent X, assigning all other values to an “Other” category. In all these situations, the user interface may present the user with the option of specifying this information for each feature detected (e.g., at step 912 of the method 900).

The user interface may provide guided assistance in transforming features. For example, a user may want to convert a continuous variable into a categorical variable, but there may be no standard conventions for that variable. By analyzing the shape of the distribution, the engine 610 may choose the optimal number of categorical bands and the points at which to place “knots” in the distribution that define the boundaries between each band. Optionally, the user may override these defaults in the user interface by adding or deleting knots, as well as moving the location of the knots.

Similarly, for features that are already categorical, the engine 610 may simplify their representation by combining one or more categories into a single category. Based on the relative frequency of each observed category and the frequency with which they appear relative to the values of other features, the engine 610 may calculate the optimal way to combine categories. Optionally, the user may override these calculations by removing original categories from a combined category and/or putting existing categories into a combined category.

In certain cases, a prediction problem may include events that occur at irregular intervals. In such cases, it may be useful to automatically create a new feature that captures how many of these events have occurred within a particular time frame. For example, in insurance prediction problems, a dataset may have records of each time a policy holder had a claim. However, in building a model to predict future risk, it may be more useful to consider how many claims a policy-holder has had in the past X years. The engine may detect such situations when it evaluates the dataset (e.g., step 908 of the method 900) by detecting data structure relationships between records corresponding to entities and other records corresponding to events. When presenting the dataset to the user (e.g., at step 910), the user interface may automatically create or suggest creating such a feature. It may also suggest a time frame threshold based on the frequency with which the event occurs, calculated to maximize the statistical dependency between this variable and the occurrence of future events, or using some other heuristic. The user interface may also allow the user to override the creation of such a feature, force the creation of such a feature, and override the suggested time frame threshold.

When the system makes predictions based on models, users may wish to review these predictions and explore unusual ones. For example, the user interface may provide a list of all or a subset of predictions for a model and indicate which ones were extreme, either in terms of the magnitude of the value of the predictor or its low probability of having that value. Moreover, it is also possible to provide insight into the reason for the extreme value. For example, in an automobile insurance risk model, a particular high value may have the reason “age <25 and marital status=single.”

In some implementations, at least a portion of the approaches described above may be realized by instructions that upon execution cause one or more processing devices to carry out the processes and functions described above. Such instructions may include, for example, interpreted instructions such as script instructions, or executable code, or other instructions stored in a non-transitory computer readable medium. The storage device may be implemented in a distributed way over a network, such as a server farm or a set of widely distributed servers, or may be implemented in a single computing device.

Embodiments of the subject matter, functional operations and processes described in this specification can be implemented in other types of digital electronic circuitry, in tangibly-embodied computer software or firmware, in computer hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible nonvolatile program carrier for execution by, or to control the operation of, data processing apparatus. Alternatively or in addition, the program instructions can be encoded on an artificially generated propagated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to suitable receiver apparatus for execution by a data processing apparatus. The computer storage medium can be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of one or more of them.

Present implementations can obtain, at least at the database and data collectors discussed above, real-time data in many categories and aggregate population data of additional category types. As one example, present implementations can obtain, but are not limited to obtaining, real-time reported cases, deaths, testing data, vaccination rates, and hospitalization rates from any suitable source external data source. Data sources are not limited to university and government databases, and those examples are presented above as non-limiting examples. As another example, present implementations can obtain, but are not limited to obtaining, real-time mobility data including movement trends over time by geography, and movement across different categories of places, such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential. As another example, present implementations can obtain, but are not limited to obtaining, real-time climate and other environmental data known to be disease drivers, including temperature, rainfall, and the like. Present implementations can also obtain, but are not limited to obtaining, static demographic data, including age, gender, race, ethnicity, population density, obesity rates, diabetes rates, and the like. Present implementations can also obtain, but are not limited to obtaining, static socio-economic data including median annual income, median educational level, median lifespan, and the like.

Although examples provided herein may have described modules as residing on separate computers or operations as being performed by separate computers, it should be appreciated that the functionality of these components can be implemented on a single computer, or on any larger number of computers in a distributed fashion.

The above-described embodiments may be implemented in any of numerous ways. For example, the embodiments may be implemented using hardware, software or a combination thereof. When implemented in software, the software code can be executed on any suitable processor or collection of processors, whether provided in a single computer or distributed among multiple computers. Further, it should be appreciated that a computer may be embodied in any of a number of forms, such as a rack-mounted computer, a desktop computer, a laptop computer, or a tablet computer. Additionally, a computer may be embedded in a device not generally regarded as a computer but with suitable processing capabilities, including a Personal Digital Assistant (PDA), a smart phone or any other suitable portable or fixed electronic device.

Such computers may be interconnected by one or more networks in any suitable form, including as a local area network or a wide area network, such as an enterprise network or the Internet. Such networks may be based on any suitable technology and may operate according to any suitable protocol and may include wireless networks, wired networks or fiber optic networks.

Also, the various methods or processes outlined herein may be coded as software that is executable on one or more processors that employ any one of a variety of operating systems or platforms. Additionally, such software may be written using any of a number of suitable programming languages and/or programming or scripting tools, and also may be compiled as executable machine language code or intermediate code that is executed on a framework or virtual machine.

In this respect, some embodiments may be embodied as a computer readable medium (or multiple computer readable media) (e.g., a computer memory, one or more floppy discs, compact discs, optical discs, magnetic tapes, flash memories, circuit configurations in Field Programmable Gate Arrays or other semiconductor devices, or other tangible computer storage medium) encoded with one or more programs that, when executed on one or more computers or other processors, perform methods that implement the various embodiments discussed above. The computer readable medium or media may be non-transitory. The computer readable medium or media can be transportable, such that the program or programs stored thereon can be loaded onto one or more different computers or other processors to implement various aspects of predictive modeling as discussed above. The terms “program” or “software” are used herein in a generic sense to refer to any type of computer code or set of computer-executable instructions that can be employed to program a computer or other processor to implement various aspects described in the present disclosure. Additionally, it should be appreciated that according to one aspect of this disclosure, one or more computer programs that when executed perform predictive modeling methods need not reside on a single computer or processor, but may be distributed in a modular fashion amongst a number of different computers or processors to implement various aspects of predictive modeling.

Computer-executable instructions may be in many forms, such as program modules, executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Typically the functionality of the program modules may be combined or distributed as desired in various embodiments.

Also, data structures may be stored in computer-readable media in any suitable form. For simplicity of illustration, data structures may be shown to have fields that are related through location in the data structure. Such relationships may likewise be achieved by assigning storage for the fields with locations in a computer-readable medium that conveys relationship between the fields. However, any suitable mechanism may be used to establish a relationship between information in fields of a data structure, including through the use of pointers, tags or other mechanisms that establish a relationship between data elements.

Also, predictive modeling techniques may be embodied as a method, of which an example has been provided. The acts performed as part of the method may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts simultaneously, even though shown as sequential acts in illustrative embodiments.

In some embodiments the method(s) may be implemented as computer instructions stored in portions of a computer's random access memory to provide control logic that affects the processes described above. In such an embodiment, the program may be written in any one of a number of high-level languages, such as FORTRAN, PASCAL, C, C++, C#, Java, JavaScript, Tcl, or BASIC. Further, the program can be written in a script, macro, or functionality embedded in commercially available software. Additionally, the software may be implemented in an assembly language directed to a microprocessor resident on a computer. The software may be embedded on an article of manufacture including, but not limited to, “computer-readable program means” such as a floppy disk, a hard disk, an optical disk, a magnetic tape, a PROM, an EPROM, or CD-ROM.

Various aspects of the present disclosure may be used alone, in combination, or in a variety of arrangements not specifically described in the foregoing, and the solution is therefore not limited in its application to the details and arrangement of components set forth in the foregoing description or illustrated in the drawings. For example, aspects described in one embodiment may be combined in any manner with aspects described in other embodiments.

The phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting.

The indefinite articles “a” and “an,” as used in the specification and in the claims, unless clearly indicated to the contrary, should be understood to mean “at least one.” The phrase “and/or,” as used in the specification and in the claims, should be understood to mean “either or both” of the elements so conjoined, i.e., elements that are conjunctively present in some cases and disjunctively present in other cases. Multiple elements listed with “and/or” should be construed in the same fashion, i.e., “one or more” of the elements so conjoined. Other elements may optionally be present other than the elements specifically identified by the “and/or” clause, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, a reference to “A and/or B”, when used in conjunction with open-ended language such as “comprising” can refer, in one embodiment, to A only (optionally including elements other than B); in another embodiment, to B only (optionally including elements other than A); in yet another embodiment, to both A and B (optionally including other elements); etc.

As used in the specification and in the claims, “or” should be understood to have the same meaning as “and/or” as defined above. For example, when separating items in a list, “or” or “and/or” shall be interpreted as being inclusive, i.e., the inclusion of at least one, but also including more than one, of a number or list of elements, and, optionally, additional unlisted items. Only terms clearly indicated to the contrary, such as “only one of or “exactly one of,” or, when used in the claims, “consisting of,” will refer to the inclusion of exactly one element of a number or list of elements. In general, the term “or” as used shall only be interpreted as indicating exclusive alternatives (i.e. “one or the other but not both”) when preceded by terms of exclusivity, such as “either,” “one of,” “only one of,” or “exactly one of” “Consisting essentially of,” when used in the claims, shall have its ordinary meaning as used in the field of patent law.

As used in the specification and in the claims, the phrase “at least one,” in reference to a list of one or more elements, should be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements. This definition also allows that elements may optionally be present other than the elements specifically identified within the list of elements to which the phrase “at least one” refers, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, “at least one of A and B” (or, equivalently, “at least one of A or B,” or, equivalently “at least one of A and/or B”) can refer, in one embodiment, to at least one, optionally including more than one, A, with no B present (and optionally including elements other than B); in another embodiment, to at least one, optionally including more than one, B, with no A present (and optionally including elements other than A); in yet another embodiment, to at least one, optionally including more than one, A, and at least one, optionally including more than one, B (and optionally including other elements); etc.

The use of “including,” “comprising,” “having,” “containing,” “involving,” and variations thereof, is meant to encompass the items listed thereafter and additional items.

Use of ordinal terms such as “first,” “second,” “third,” etc., in the claims to modify a claim element does not by itself connote any priority, precedence, or order of one claim element over another or the temporal order in which acts of a method are performed. Ordinal terms are used merely as labels to distinguish one claim element having a certain name from another element having a same name (but for use of the ordinal term), to distinguish the claim elements. Having thus described several aspects of at least one embodiment of this solution, it is to be appreciated that various alterations, modifications, and improvements will readily occur to those skilled in the art. Such alterations, modifications, and improvements are intended to be part of this disclosure, and are intended to be within the spirit and scope of the solution.

L. Terminology

The phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting.

As used herein, “image data” may refer to a sequence of digital images (e.g., video), a set of digital images, a single digital image, and/or one or more portions of any of the foregoing. A digital image may include an organized set of picture elements (“pixels”) stored in a file. Any suitable format and type of digital image file may be used, including but not limited to raster formats (e.g., TIFF, JPEG, GIF, PNG, BMP, etc.), vector formats (e.g., CGM, SVG, etc.), compound formats (e.g., EPS, PDF, PostScript, etc.), and/or stereo formats (e.g., MPO, PNS, JPS).

As used herein, “non-image data” may refer to any type of data other than image data, including but not limited to structured textual data, unstructured textual data, categorical data, and/or numerical data.

As used herein, “natural language data” may refer to speech signals representing natural language, text (e.g., unstructured text) representing natural language, and/or data derived therefrom.

As used herein, “speech data” may refer to speech signals (e.g., audio signals) representing speech, text (e.g., unstructured text) representing speech, and/or data derived therefrom.

As used herein, “auditory data” may refer to audio signals representing sound and/or data derived therefrom.

As used herein “time-series data” may refer to data having the attributes of “time-series data.”

As used herein, the term “machine learning model” may refer to any suitable model artifact generated by the process of training a machine learning algorithm on a specific training data set. Machine learning models can be used to generate predictions.

As used herein, the term “machine learning system” may refer to any environment in which a machine learning model operates. A machine learning system may include various components, pipelines, data sets, other infrastructure, etc.

As used herein, the term “development” with regard to a machine learning model may refer to construction of the machine learning model. Machine learning models may be constructed by computers using training data sets. Thus, “development” of a machine learning model may refer to training of the machine learning model using a training data set. In some cases (generally referred to as “supervised learning”), a training data set used to train a machine learning model can include known outcomes (e.g., labels). In alternative cases (generally referred to as “unsupervised learning”), a training data set does not include known outcomes.

As used herein, “data analytics” may refer to the process of analyzing data (e.g., using machine learning models or techniques) to discover information, draw conclusions, and/or support decision-making. Species of data analytics can include descriptive analytics (e.g., processes for describing the information, trends, anomalies, etc. in a data set), diagnostic analytics (e.g., processes for inferring why specific trends, patterns, anomalies, etc. are present in a data set), predictive analytics (e.g., processes for predicting future events or outcomes), and prescriptive analytics (processes for determining or suggesting a course of action).

The term “approximately”, the phrase “approximately equal to”, and other similar phrases, as used in the specification and the claims (e.g., “X has a value of approximately Y” or “X is approximately equal to Y”), should be understood to mean that one value (X) is within a predetermined range of another value (Y). The predetermined range may be plus or minus 20%, 10%, 5%, 3%, 1%, 0.1%, or less than 0.1%, unless otherwise indicated.

The indefinite articles “a” and “an,” as used in the specification and in the claims, unless clearly indicated to the contrary, should be understood to mean “at least one.” The phrase “and/or,” as used in the specification and in the claims, should be understood to mean “either or both” of the elements so conjoined, i.e., elements that are conjunctively present in some cases and disjunctively present in other cases. Multiple elements listed with “and/or” should be construed in the same fashion, i.e., “one or more” of the elements so conjoined. Other elements may optionally be present other than the elements specifically identified by the “and/or” clause, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, a reference to “A and/or B”, when used in conjunction with open-ended language such as “comprising” can refer, in one embodiment, to A only (optionally including elements other than B); in another embodiment, to B only (optionally including elements other than A); in yet another embodiment, to both A and B (optionally including other elements); etc.

As used herein, “or” should be understood to have the same meaning as “and/or” as defined above. For example, when separating items in a list, “or” or “and/or” shall be interpreted as being inclusive, i.e., the inclusion of at least one, but also including more than one, of a number or list of elements, and, optionally, additional unlisted items. Only terms clearly indicated to the contrary, such as “only one of or “exactly one of,” or, when used in the claims, “consisting of,” will refer to the inclusion of exactly one element of a number or list of elements. In general, the term “or” as used shall only be interpreted as indicating exclusive alternatives (i.e. “one or the other but not both”) when preceded by terms of exclusivity, such as “either,” “one of,” “only one of,” or “exactly one of” “Consisting essentially of,” when used in the claims, shall have its ordinary meaning as used in the field of patent law.

As used herein, the phrase “at least one,” in reference to a list of one or more elements, should be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements. This definition also allows that elements may optionally be present other than the elements specifically identified within the list of elements to which the phrase “at least one” refers, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, “at least one of A and B” (or, equivalently, “at least one of A or B,” or, equivalently “at least one of A and/or B”) can refer, in one embodiment, to at least one, optionally including more than one, A, with no B present (and optionally including elements other than B); in another embodiment, to at least one, optionally including more than one, B, with no A present (and optionally including elements other than A); in yet another embodiment, to at least one, optionally including more than one, A, and at least one, optionally including more than one, B (and optionally including other elements); etc.

The use of “including,” “comprising,” “having,” “containing,” “involving,” and variations thereof, is meant to encompass the items listed thereafter and additional items.

Use of ordinal terms such as “first,” “second,” “third,” etc., in the claims to modify a claim element does not by itself connote any priority, precedence, or order of one claim element over another or the temporal order in which acts of a method are performed. Ordinal terms are used merely as labels to distinguish one claim element having a certain name from another element having a same name (but for use of the ordinal term), to distinguish the claim elements. 

What we claim is:
 1. A method, comprising: receiving, by one or more processors, a secondary dataset to be combined with a primary dataset for generation of a training dataset, the primary dataset comprising a plurality of data records where at least one data record corresponds to a time-of-prediction value corresponding to a timestamp at which at least one data record was used to generate a prediction, and the secondary dataset comprising a plurality of features where at least one feature corresponds to a timestamp value; selecting, by the one or more processors, a feature within the secondary dataset with a timestamp that precedes or matches a time-of-prediction value for a corresponding data record within the primary dataset; generating, by the one or more processors, the training dataset that comprises the primary dataset and the selected feature; and training, by the one or more processors, a model using the generated training dataset.
 2. The method of claim 1, wherein the secondary dataset is received from a user computing device.
 3. The method of claim 1, further comprising: executing, by the one or more processors, a feature transformation protocol to at least one feature within the secondary dataset before the at least one feature is added to the primary dataset.
 4. The method of claim 3, wherein the feature transformation protocol is dependent upon a feature type of the at least one feature.
 5. The method of claim 3, wherein the one or more processors extract a new feature from the at least one feature within the secondary dataset by executing the feature transformation protocol, wherein the one or more processors add the new feature to the primary dataset.
 6. The method of claim 1, further comprising: revising, by the one or more processors, a time limit of at least one feature derivation window for at least one feature within the secondary dataset.
 7. The method of claim 1, further comprising: revising, by the one or more processors, a time-of-prediction value for at least one data record within the primary dataset.
 8. The method of claim 1, further comprising: for each pair of features within the training dataset, generating, by the one or more processors, an interpretability value by analyzing at least one of: a correlation between the pair of features, a comparison of coefficients for the pair of features, a comparison of signs for each feature in the pair of features, or a number of unique values within each feature in the pair of features.
 9. The method of claim 1, further comprising: determining, by the one or more processors, a recipe for deriving an engineered feature within the primary dataset, the recipe comprising at least one of SQL statements or code; and in response to receiving a request to generate a new prediction for a new set of features using the model, deriving, by the one or more processors, a new engineered feature by applying the recipe to the new set of features.
 10. The method of claim 9, wherein the one or more processors apply the recipe at the time-of-prediction value for the new set of features.
 11. The method of claim 9, further comprising: identifying, by the one or more processors, at least one engineered feature not used by the model to generate the new prediction; and removing, by the one or more processors, statement or code associated with the identified at least one data feature from the recipe.
 12. The method of claim 9, wherein the one or more processors applies as least one engineered feature associated with a previous session to the model to generate the new prediction.
 13. The method of claim 1, further comprising: generating, by the one or more processors, a lineage map comprising a relationship between at least one data record within the secondary dataset and at least one feature within the primary dataset.
 14. The method of claim 13, further comprising: presenting, by the one or more processors on a graphical user interface, a graphical representation of the primary dataset and at least one data record of the secondary dataset visually related in accordance with the lineage map.
 15. The method of claim 1, further comprising: removing, by the one or more processors, at least one feature based at least on one of: determining that the at least one feature is missing at least one value, determining that the at least one feature corresponds to a constant value, determining that the at least one feature is a duplicate value, or determining that the at least one feature has a data size that is beyond a threshold.
 16. The method of claim 1, further comprising: removing, by the one or more processors, at least one feature from the training dataset based at least on one of: determining that the at least one feature has an impact that satisfies a first threshold, or determining that the at least one feature has a quality score that satisfies a second threshold.
 17. The method of claim 16, wherein the one or more processors calculate a score based at least in part on a shapley value to determine whether the at least one feature has the impact that satisfies the first threshold.
 18. A computer system comprising: one or more processors coupled to a memory and a model, the one or more processors configured to: receive a secondary dataset to be combined with a primary dataset for generation of a training dataset, the primary dataset comprising a plurality of data records where at least one data record corresponds to a time-of-prediction value corresponding to a timestamp that at least one data record was used to generate a prediction, and the secondary dataset comprising a plurality of features where at least one feature corresponds to a timestamp value; select a feature within the secondary dataset with a timestamp that precedes or matches a time-of-prediction value for a corresponding data record within the primary dataset; generate the training dataset that comprises the primary dataset and the selected feature; and train the model using the generated training dataset.
 19. The computer system of claim 18, wherein the secondary dataset is received from a user computing device.
 20. A non-transitory computer-readable storage device storing instructions that, when executed by one or more processors, cause the one or more processors to: receive a secondary dataset to be combined with a primary dataset for generation of a training dataset, the primary dataset comprising a plurality of data records where at least one data record corresponds to a time-of-prediction value corresponding to a timestamp that at least one data record was used to generate a prediction, and the secondary dataset comprising a plurality of features where at least one feature corresponds to a timestamp value; select a feature within the secondary dataset with a timestamp that precedes or matches a time-of-prediction value for a corresponding data record within the primary dataset; generate the training dataset that comprises the primary dataset and the selected feature; and train the model using the generated training dataset. 